Latest / The Pricing Marketing Podcast with Fexingo: Price Anchoring, Bundling, and Conversion Pricing / How Dynamic Pricing Traps Your Wallet
Transcript
- Lucas: Imagine you are booking a flight from Chicago to New York on a Tuesday morning. You search for a seat, see a price of four hundred dollars, and close the tab because it feels steep. An hour later, you open it again, and suddenly that same seat is five fifty. You didn't wait. The plane didn't fill up. The only thing that changed was the fact that you had already looked. Luna: It feels like punishment for curiosity, doesn't it? Like the system knows I want to go and is holding my hesitation against me. Lucas: That is exactly what is happening, and it is called dynamic pricing driven by personalized algorithms. In traditional economics, we talk about supply and demand moving as a broad wave. But today, the wave has been broken into millions of tiny ripples, each one targeting an individual user based on their browsing history, device type, and even the time of day they check prices. Luna: So it is not just about scarcity anymore. It is about predicting exactly how much someone is willing to pay before they even click buy. Lucas: Precisely. Economists call this first-degree price discrimination, or perfect price discrimination. Ideally, this means every customer pays exactly their maximum willingness to pay. The seller captures the entire consumer surplus. In the old world, this required a skilled salesman negotiating face to face. Today, it requires a server farm and a cookie tracker. Luna: But does it actually work? Or are companies just guessing wildly and hoping for the best? Lucas: They are not guessing. They are testing. Let us look at ride-sharing apps. If you request a car during rain, the price jumps. That is basic surge pricing. But if you have a premium subscription, or if you use a newer iPhone versus an older Android model, the base rate might differ slightly. The algorithm is constantly A-B testing your elasticity. If you book immediately without comparing three other options, the model learns you are less price-sensitive. Luna: That sounds terrifyingly efficient. It assumes that my convenience is worth more than the guy who checks reviews for twenty minutes. Lucas: It assumes that speed has a value, and that value varies by person. The problem is, the data used to calculate that speed isn't always transparent. You might be paying more simply because your location shows you are in a high-income zip code, or because your browser history shows you recently searched for luxury hotels. Luna: So the anchor isn't just the product. The anchor is my own profile. Lucas: Exactly. And this shifts the power dynamic entirely. In a static pricing model, the price is a signal of quality or cost. In a dynamic, personalized model, the price is a signal of vulnerability. The business knows something about you that you haven't told them explicitly. Luna: It makes me feel like I am being audited before I even make a purchase. Lucas: Let us dig into the mechanism. How do they actually build this profile? It starts with simple signals. Do you use incognito mode? Studies show that sometimes, yes, private browsing can lower prices because the algorithm sees you as a cold lead rather than a returning warm prospect. But most people don't use incognito mode. Luna: I never use it. I thought it was just for hiding things from my spouse or my boss. Lucas: For many, it is. But for the algorithm, it is a flag. If you appear as a new visitor, the baseline price might be lower to encourage conversion. If you appear as a known entity who has abandoned carts twice before, the algorithm assumes you are closer to buying and raises the price to capture that intent. It is a subtle nudge, but over thousands of transactions, it adds up. Luna: So abandonment triggers a price hike? That seems counterintuitive. Usually, we get discount emails when we abandon. Lucas: That is the second layer. Some companies use discounts to recover lost sales. Others use price increases to pressure immediate action. It depends on the industry and the margin structure. Airlines almost always raise prices as departure nears. E-commerce sites might offer a coupon to bring you back. But the underlying engine is the same: predicting your next move. Luna: Is there a point where this becomes illegal? Or is it just a gray area of 'buyer beware'? Lucas: Currently, in the United States, it is largely legal. There are no federal laws banning personalized pricing based on browsing data. The European Union is tightening regulations under GDPR and the Digital Markets Act, requiring more transparency about automated decision-making. But here, the default is still permissive. Businesses argue it helps match supply with demand efficiently. Luna: Efficient for whom though? If I end up paying thirty percent more because I clicked too fast, is that really efficiency? Lucas: It is efficiency for the seller. The consumer surplus shrinks, and the producer surplus grows. From a macroeconomic view, some argue it reduces waste by ensuring seats aren't empty. But from a consumer welfare view, it feels like exploitation. It removes the fixed reference point that allows us to budget and plan. Luna: Without a fixed reference, I can't tell if I'm getting a good deal or just a bad one. Lucas: Right. You lose the ability to benchmark. When the price changes every time you refresh, you can't say, 'This is a fair price.' You can only say, 'This is the price right now.' It creates a sense of instability that keeps you engaged but anxious. Luna: Anxiety drives purchases. I know that feeling. When I think the price might jump, I buy faster. Lucas: And that is the trap. The algorithm is designed to create urgency. Even if the price hasn't jumped yet, the that it will jump is enough to shorten your consideration window. You stop comparing features and start comparing clock times. Luna: So the product becomes secondary. The timer becomes primary. Lucas: Exactly. And this is where it gets interesting for marketers. You don't need to change the product. You just need to change the presentation of the price. Show a countdown timer. Show 'three people viewing this right now.' These are psychological triggers that amplify the effect of dynamic pricing. Luna: It feels manipulative. Like they are hacking my decision-making process. Lucas: In a way, they are. But let us look at the flip side. For businesses, especially small ones, this can be a survival tool. A boutique hotel with empty rooms on a Tuesday can lower prices dynamically to fill them, rather than keeping a static high price and losing revenue. It helps optimize asset utilization. Luna: Okay, I see the benefit for the seller. Empty rooms are worse than discounted rooms. But does the average consumer really benefit from this complexity? Lucas: Some do. Price-sensitive consumers who shop around, use incognito mode, or buy at off-peak hours can save money. The algorithm rewards flexibility. If you are rigid in your timing and channel, you pay the premium. So it is a tax on inconvenience. Luna: A tax on my time. Which is ironic, because I am trying to save time by buying quickly. Lucas: Yes. The paradox of choice and the paradox of convenience collide here. The tools that make shopping easier also make you more predictable. And predictability is profitable for the platform. Luna: What can we actually do about it? Can we opt out? Lucas: Not easily. You can clear cookies, use different devices, or try incognito mode. But these are band-aids. The deeper issue is the lack of transparency. We don't know what data points are being used. Is it my IP address? My past purchases? My social media connections? Without visibility, we cannot negotiate. Luna: So we are flying blind while they have the map. Lucas: Precisely. And as AI gets better at predicting behavior, this gap will widen. We are moving toward a future where prices are not just dynamic, but hyper-personalized. You might see a completely unique price that no one else in the world sees. Luna: That sounds like the end of the public square for commerce. Every transaction is private and unique. Lucas: It is. And it raises ethical questions about fairness. If two people earn the same salary, but one lives in a digital-rich neighborhood and the other in a digital-poor one, should they pay different prices for the same service? Most people would say no. But the algorithm doesn't care about equity. It cares about optimization. Luna: Optimization without ethics is just extraction. Lucas: That is a sharp way to put it. And it is why regulators are starting to pay attention. The FTC has hinted at reviewing pricing practices. The EU is enforcing stricter rules. But in the US, we are still in the wild west phase. Luna: So for the listener, the takeaway is awareness. Know that the price you see is not the price everyone sees. Lucas: Exactly. Be skeptical of the first number you see. Use multiple devices if possible. Wait if you can. Understand that your attention is the currency being traded alongside the product. Luna: And remember that your patience has value. Sometimes waiting is the best negotiation tactic. Lucas: Well said. Before we wrap up, I want to mention something important about how this show operates. Luna: Right, because discussing corporate algorithms makes us appreciate independent voices even more. Lucas: Exactly. We don't take ads. We rely on a small group of listeners who believe in ad-free business analysis. If you find value in these deep dives, consider supporting us monthly at buy me a coffee dot com slash fexingo. It keeps the lights on and the research going. Luna: It is the best way to ensure we stay independent and keep asking these tough questions without corporate influence. Lucas: Thanks for considering it. Now, back to the topic. One final thought on dynamic pricing. Luna: Go ahead. Lucas: As we move forward, the line between marketing and surveillance will blur further. The question isn't just how companies set prices, but what rights consumers have to resist those algorithms. Luna: It is a fundamental shift in the social contract of commerce. We will have to watch this space closely. Lucas: Agreed. Until next time, keep your cookies clean and your skepticism high.