Latest / Pricing Power Podcast with Fexingo: How Businesses Set Prices, Raise Margins, and Win Customers / The Hidden Cost of Price Optimization Algorithms
Transcript
- Lucas: Let’s talk about the invisible hand that’s been squeezing your wallet lately. It isn’t just inflation or supply chain snarls. It is the fact that the price you see for a flight, a hotel room, or even a pair of sneakers right now is likely being calculated by a computer in real time, adjusting to your browsing history, your location, and the speed at which you’re clicking through checkout. Luna: Right, and what makes it so frustrating is that we know it is happening, but we can’t really prove it in the moment. You just get this creeping sense that you are being charged more than the person next to you. Lucas: Exactly. And that feeling is costing businesses money. For years, companies treated dynamic pricing as a purely mathematical exercise to maximize yield. But as October rolls in here in late twenty twenty six, we are seeing a distinct pivot. Major retailers and service providers are realizing that if their algorithms are too aggressive, they aren’t just optimizing revenue; they are actively destroying brand equity. Luna: So you are saying the era of pure profit maximization via algorithms is hitting a wall because customers are voting with their feet? Lucas: Precisely. Let’s look at a specific example. There was a mid-sized hotel chain, let’s call them Horizon Hospitality, that implemented a sophisticated dynamic pricing engine last year. Within three months, their average daily rate went up by fifteen percent. Sounds like a win, right? But their direct booking volume dropped by twenty percent. Customers who felt they were being price-gouged simply switched to third-party aggregators where the prices appeared more stable, or they just stopped going altogether. Luna: That is a huge number. So they traded long-term customer lifetime value for short-term margin spikes. Lucas: They did. And the irony is that the algorithm was working perfectly according to its original instructions. It was capturing every ounce of consumer surplus. But it failed to account for the emotional cost of perceived unfairness. When I book a hotel and see the price jump by fifty dollars every ten minutes while I’m trying to decide, I don’t think I’m getting a good deal. I think I’m being played. Luna: It triggers this immediate defensive reaction. I start looking for reasons to leave, rather than reasons to stay. Lucas: And that is where the modern approach is shifting. Companies are moving from opaque dynamic pricing to transparent, value-based pricing. Take the airline industry. Some carriers are now using 'fare families' that are clearly defined upfront. You pay more for flexibility, not because the system detected you had a credit card with a high limit. It is a subtle but massive difference in framing. Luna: So instead of hiding the price hike behind an algorithm, they put it on a menu? That feels much less predatory. Lucas: It does. And it allows the customer to retain agency. If I want to change my flight, I pay a fee. If I want a refundable ticket, I pay more. It is honest. The problem with the old model was that it relied on information asymmetry. The company knew exactly what I would pay. I didn’t know until it was too late. Luna: But isn’t there a risk that transparency kills some of the efficiency gains? If everyone knows the price structure, won’t arbitrageurs just game the system? Lucas: There is always a trade-off. But the data suggests that the loss of trust is far more expensive than the slight inefficiency in pricing. A study out of Stanford Business School last year showed that brands that communicated their pricing logic clearly saw a twelve percent increase in repeat purchase rates, even if their initial conversion rates were slightly lower. People come back when they feel respected. Luna: Respect as a currency. That is a powerful way to put it. Lucas: It is. And this brings us to the role of AI in this equation. Everyone talks about AI replacing jobs, but in pricing, AI is becoming the gatekeeper of fairness. Or the enforcer of greed, depending on how you configure it. If you program your AI to maximize short-term revenue, it will extract every penny. If you program it to maximize long-term customer retention, it will leave money on the table sometimes to build goodwill. Luna: So the objective function of the algorithm is actually the most important business decision a CEO can make right now. Lucas: I would argue yes. Most companies still treat pricing software as a black box. They plug in the data, press go, and hope for the best. But that is lazy management. You need to define what 'best' means. Is it revenue today? Or market share in five years? Luna: It sounds like a lot of CFOs are terrified of losing control. They want the algorithm to do the work, but they also want to blame it when things go wrong. Lucas: Spot on. And then there is the regulatory angle. In Europe, the rules around dynamic pricing are getting tighter. They are looking at whether these algorithms constitute anti-competitive behavior or deceptive practices. If a platform charges different prices to different users for the same product without clear justification, that is walking a very thin line legally. Luna: Do you think US regulators are going to follow suit soon? Or is it still too early for that kind of intervention? Lucas: It is possible. The Federal Trade Commission has been vocal about digital marketplace fairness. If they decide that algorithmic price discrimination is harming consumers, they could force companies to disclose their pricing logic. Imagine having to explain to a court why you charged me forty dollars more for the same shirt because I live in a wealthier zip code. Luna: That would be a nightmare for any marketing team. It forces a level of honesty that most brands aren't ready for. Lucas: It does. And honestly, many brands aren't ready because they haven't thought it through. They just want to squeeze margins. But the smartest companies are already ahead of this curve. They are building pricing systems that are explainable. They can tell you why a price changed. And that transparency is becoming a competitive advantage. Luna: It reminds me of how people react to surge pricing in ride shares. The anger is almost visceral. It feels like a betrayal of the service contract. Lucas: It is. And ride-share companies have learned that lesson the hard way. They introduced caps and clearer notifications. They realized that if you punish your customers for peak demand too aggressively, they will just switch to a competitor or use public transit. You cannot monetize frustration indefinitely. Luna: So the key is balancing efficiency with empathy. Can you quantify that balance? Lucas: You can measure the sentiment. Social listening tools can track mentions of 'price gouging' or 'unfair' alongside your brand name. If those metrics spike during a pricing adjustment window, you know your algorithm has gone too far. You need to dial it back. It is a feedback loop that human intuition alone couldn't manage at scale. Luna: Which means the algorithm needs to be tuned by humans, not just set and forgotten. Lucas: Absolutely. It is a partnership between data science and psychology. You need economists who understand elasticity, but you also need psychologists who understand fairness. Without that second layer, you are just a machine extracting value. With it, you are building a brand. Luna: This conversation has been incredibly useful for anyone running a subscription service or e-commerce store. We need to audit our own pricing logic. Lucas: If these conversations have moved your work forward in some small way, please consider supporting the show. Listener support is what keeps this network ad-free and allows us to dig deep into these topics without corporate interference. You can find us at buy me a coffee dot com slash fexingo — that's F. E. X. I. N. G. O. Luna: Thanks for keeping the lights on and the signal clear. Lucas: Back to the topic. Let’s talk about implementation. If you are a small business owner listening to this, you might not have a billion-dollar budget for AI pricing software. What are the low-tech ways to test if your pricing is causing friction? Luna: Simple A/B testing. Show different price points to different segments of your audience and monitor the churn rate. If the higher price segment drops off faster than you expected, you’ve got your answer. Lucas: Yes, and look at your customer support tickets. Are people asking about prices more than usual? Are they complaining about hidden fees? Those are early warning signs. Your support team is your front-line intelligence. Listen to them. Luna: And don’t forget to communicate changes proactively. If you have to raise prices, tell people why. Supply costs went up, quality improved. Give them a reason that makes sense. Lucas: Context is everything. A price hike without context feels like theft. A price hike with context feels like a necessary adjustment. The difference is massive in terms of customer retention. Luna: It comes down to respect. Treating the customer as a partner in the transaction, not a resource to be mined. Lucas: Exactly. And in a world where algorithms can do almost anything, choosing restraint is often the smartest business move you can make. Luna: It is interesting to think about how much power we, as consumers, actually have. We just have to vote with our wallets and our attention. Lucas: We do. And the market is starting to reward the companies that remember that. It might take a while for all the legacy players to catch up, but the trend is clear. Luna: What do you think the next big shift will be? Will we see a return to fixed pricing as a luxury feature? Lucas: Possibly. Just like handwritten letters are a premium product now, fixed, predictable pricing might become a high-end offering. You pay extra for the certainty that the price won’t change tomorrow. Luna: That would be a fascinating niche to watch. Certainty in an uncertain world. Lucas: It really is. And that is something worth thinking about as we navigate the rest of the quarter. How are you pricing your own relationships with the brands you love?