Latest / Pricing Power Podcast with Fexingo: How Businesses Set Prices, Raise Margins, and Win Customers / How Dynamic Pricing Changes Everything for Retailers
Transcript
- Lucas: So you're browsing Amazon for a portable Bluetooth speaker. You check it in the morning — seventy-nine dollars. You add it to your cart but don't buy. You check again at lunch — seventy-two fifty. By the time you get home, it's back up to seventy-nine. What just happened? Luna: You just got hit by dynamic pricing. And honestly, most people don't realize how fast those prices are moving under the hood. Lucas: Exactly. Amazon changes prices on millions of products every day — some estimates say every ten minutes for top-selling items. This isn't manual. It's algorithmic. A machine watches competitor prices, demand signals, inventory levels, even the time of day, and tweaks in real time. Luna: And this isn't just Amazon. Uber, airlines, hotels — they've been doing this for years. But retail is where it's getting really interesting, because the margins are thinner and the stakes higher. Lucas: Let's ground this in a concrete case. A few years ago, a third-party seller on Amazon's marketplace — let's call them 'GadgetCo' — listed a bestselling kitchen gadget at thirty-nine ninety-nine. Steady sales, decent margin, maybe forty percent. Then Amazon's algorithm noticed a competitor dropped their price to thirty-four ninety-nine. Luna: So Amazon's own listing — the one sold by Amazon itself — automatically matched that lower price, or went even lower? Lucas: In this case, Amazon's system dropped its price to thirty-three ninety-nine. Within hours, GadgetCo's sales cratered because Amazon's listing showed up first. They had to match or lose the buy box entirely. They matched, but their margin evaporated. In one week, they went from forty percent margin to barely ten percent. Luna: That's brutal. And it's the reality for hundreds of thousands of sellers. You're not just competing with other merchants — you're competing with an algorithm that never sleeps and has better data than you do. Lucas: And it raises a fundamental question: who benefits? Amazon says it gives consumers the best price. And yes, in the short term, you pay less. But if small sellers get squeezed out, choice shrinks, and over time prices could actually rise. Luna: There's also the creepy factor. We've all heard stories about prices changing based on your location or browsing history. A study from a few years ago found that a travel site showed higher hotel prices to users on Apple devices versus Windows. Dynamic pricing can feel like discrimination. Lucas: That's the line between smart pricing and predatory pricing. And it's not always clear where that line is. Let's step back and look at where this all came from. The airline industry essentially invented modern dynamic pricing in the 1980s with yield management systems. Luna: Right — American Airlines' Sabre system. They realized that an empty seat is a wasted asset, so you'd rather sell it cheap at the last minute than fly with it empty. That logic applies to any perishable inventory. Lucas: Hotels, rental cars, concert tickets — same principle. But retail is different because inventory isn't perishable in the same way. A blender that sits on a shelf for a month still has value. So the algorithm has to balance speed of sale with margin preservation. Luna: And that's where machine learning comes in. Amazon's system doesn't just react — it predicts. It uses historical data to forecast demand at different price points, then selects the price that maximizes either revenue or profit over a given time horizon. Lucas: I've seen numbers from a major grocery chain that tested dynamic pricing on a few hundred products. They used an algorithm that adjusted prices based on time of day and local demand. Their revenue on those items went up by about eight percent, and they reduced waste on perishables by fifteen percent. Luna: So for a grocery store with razor-thin margins, that's huge. But the consumer perception risk is real. If you go to the store every Tuesday and suddenly eggs cost thirty cents more because the algorithm thinks you'll pay it, that erodes trust. Lucas: And trust is the thing retailers are most afraid of losing. A survey from last year found that sixty-one percent of consumers said they'd stop shopping at a retailer if they discovered prices changed based on their personal data. Luna: Which is why most retailers that do dynamic pricing keep it subtle. They don't advertise it. They frame it as 'everyday low prices' or 'limited-time offers' rather than admitting the price you see is tailored to you. Lucas: That's a fascinating tension. The technology is incredibly efficient — it can match supply and demand more precisely than any human buyer. But the ethics of it are still being figured out. And regulation is starting to catch up. Luna: The EU has already started looking at algorithmic pricing under its Digital Markets Act. There's concern that dynamic pricing could facilitate collusion — if multiple retailers use the same pricing software, they could effectively coordinate prices without ever talking to each other. Lucas: That's a real antitrust issue. In 2015, there was a case where several poster sellers on Amazon were using the same repricing algorithm, and it ended up setting prices at artificially high levels across the board. The algorithm was effectively acting as a cartel. Luna: So the same tool that can lower prices for consumers can also raise them if used badly. It's all about incentives and oversight. Lucas: Speaking of tools that help people make better decisions — and I mean this in a very different context — a couple of dollars a month is genuinely what keeps these conversations going. If you've gotten something out of this show, buy me a coffee dot com slash fexingo is a simple way to support it. Luna: Yeah, it's the smallest thing that makes a real difference. Keeps us independent and ad-free, which we love. Lucas: Anyway, back to the algorithms. Let's talk about what happens when dynamic pricing goes from subtle to aggressive. There's a case from 2020 where a major retailer's system raised the price of a popular board game from twenty-five dollars to seventy dollars during the holiday season. Luna: That's the 'surge pricing' moment. And people noticed — they shared screenshots on social media. The retailer had to apologize and refund the difference. It was a PR disaster. Lucas: Right. So there's a lesson here: dynamic pricing works best when it's invisible. The moment customers feel like they're being taken advantage of, the whole system backfires. Smart companies build in guardrails — price ceilings, frequency caps, human oversight. Luna: And transparency helps too. Some businesses, like hotels and airlines, have conditioned us to expect fluctuating prices. But a grocery store doing it still feels unfair because we don't understand the logic. If you explain 'this price is lower because it's close to closing time and we'd rather sell it than throw it away', that makes sense. Lucas: Exactly. The framing matters as much as the price itself. And that's where the pricing power really lies — not in the algorithm alone, but in how you communicate the value. Luna: So what's the takeaway for a small business owner listening? Should they use dynamic pricing or stay away? Lucas: I'd say: use it, but with boundaries. Start with a simple rule — like lowering prices on slow-moving inventory or raising them during peak demand — and test it on a small set of products. Don't let the algorithm run wild. And never, ever personalize prices based on customer data unless you're prepared to defend that practice publicly. Luna: Solid advice. And keep an eye on what Amazon does next. They're constantly refining their system, and whatever they do, other retailers will follow. Lucas: That's the thing about pricing power — it's never static. The moment you think you've figured it out, the algorithm changes, the competitor moves, or the customer catches on. The best you can do is stay curious and keep testing. Luna: And maybe buy that Bluetooth speaker now, before it goes up again. Lucas: Or wait till lunch — you never know.