Latest / Fintech Conversations with Fexingo: Banking, Payments, and Financial Software Companies / How Fintechs Use Real-Time Merchant Data for Dynamic Pricing
Transcript
- Lucas: It’s episode 100 of Fintech Conversations with Fexingo, and in the spirit of a milestone, I wanted to look at something that’s quietly reshaping how thousands of small businesses set their prices — dynamic pricing powered by real-time merchant data. Luna: Dynamic pricing — not exactly new. Airlines and hotels have done it for decades. But you’re saying fintechs are bringing it to the corner pizzeria? Lucas: Exactly. And the key difference is the data stream. Airlines use historical booking curves. But platforms like Toast and Square have real-time transaction data — what’s selling, at what price, at what time of day, even what the weather is. They can feed that into a pricing engine that adjusts menu prices dynamically. Luna: So a slice of pepperoni might cost more at noon than at 3 PM? That feels… aggressive. Do customers actually accept that? Lucas: They do, if it’s done transparently. There’s a famous case — I’ll use a real one — a Chicago pizzeria called Paulie Gee’s Logan Square. They started using a dynamic pricing tool built on top of their Toast POS. The system would increase prices by about 10% during peak dinner rush, then drop them back down during slower afternoon hours. Over six months, their revenue per seat went up 12%, and customer satisfaction scores didn’t drop. Luna: Wait — customer satisfaction didn’t drop? I’d be annoyed if my pizza cost more than the person next to me. Lucas: Right, but the key is that the price change isn’t per person — it’s per time window. So everyone who orders between 6 and 8 PM pays the same premium. The menu board shows the current price, and it’s the same for everyone in that moment. No discrimination, just time-based variation. Luna: Okay, that makes more sense. Still, you need to train your staff and your customers. How did they pull it off? Lucas: They used a tool from a fintech called Sauce Pricing — I’m not making that name up. It integrates directly with Toast’s API. The restaurant sets parameters — floor price, ceiling price, sensitivity to demand — and the algorithm adjusts within those bounds. Paulie Gee’s set a cap of $4 for a slice, floor of $3.25. So the range was narrow enough that customers didn’t feel gouged. Luna: Seventy-five cents difference. That’s the kind of margin that adds up over thousands of slices. Lucas: Exactly. And the fintech part is crucial: Toast processes the payments, so it knows exactly when sales happen. That real-time signal is what makes the pricing adjustment responsive. If a thunderstorm suddenly clears the street, the algorithm drops prices to attract the few people walking by. Luna: So it’s not just about peak pricing — it’s about filling downtime too. That’s actually more valuable for most restaurants. Lucas: Right. Restaurants run on thin margins — typically 3 to 5 percent. Lifting revenue by 12% on the same fixed costs is enormous. And the data isn’t just internal. Some fintechs are now pulling in competitor pricing data from public sources. If the pizza shop across the street drops their price, your system can alert you or even adjust automatically. Luna: That’s getting into price-matching territory. Where’s the line between smart pricing and a race to the bottom? Lucas: That’s the billion-dollar question. The fintechs I’ve spoken with say they design algorithms to optimize for profit, not just volume. So if a competitor drops price, the algorithm might decide it’s better to hold — maintain brand perception or protect margin — rather than match. It’s all configurable. Luna: Let’s step back — who else besides restaurants is doing this? You mentioned Square earlier. Lucas: Square has a product called Square for Retail that offers dynamic pricing for inventory clearance. If a clothing item hasn’t sold in 60 days, the system automatically drops the price by a percentage each week until it moves. That’s been around for a while. But the newer wave is real-time, demand-driven pricing, not just clearance. Luna: Give me another example outside restaurants. Lucas: A good one is parking. Fintechs like ParkMobile or SpotHero use demand data to adjust parking rates in real time. If a concert lets out, the price for nearby spots jumps. That’s essentially dynamic pricing with payments data — the fintech knows exactly when people are paying, so it can optimize the next price. Luna: That feels less controversial — parking is already variable. But what about groceries? Are we going to see milk prices change by the hour? Lucas: Probably not milk. But perishable items — think bakery goods, deli items — some grocers are experimenting with real-time markdowns through apps like Flashfood. The fintech layer is payment integration: you see the discount, you pay in the app, you pick up. That’s dynamic pricing tied to inventory freshness. Luna: So the common thread is that the fintech owns the payment relationship, so they have the data to power the pricing engine. That’s a powerful moat. Lucas: It is. And it’s why we’re seeing traditional POS companies like NCR and Clover adding dynamic pricing modules. They don’t want to lose that data stream to a third-party fintech. Luna: What about the risk of backlash? I remember when Uber’s surge pricing made people furious. How do fintechs avoid the PR disaster? Lucas: Transparency is huge. Some restaurants display a notice: ‘Prices may vary by demand to help us serve you better.’ Others cap the maximum change — like the Paulie Gee’s example. And crucially, the algorithm is designed to avoid sudden spikes. It’s more like a gentle wave than a surge. Luna: Let’s talk about the data privacy angle. If Toast knows what I’m buying and when, and they’re using that to set prices, is that a violation? Lucas: So far, the regulatory view is that aggregated, anonymized transaction data used to set a menu price for everyone doesn’t raise the same red flags as personalized pricing — which is when you show different prices to different individuals based on their data. That’s where regulators are watching carefully. Luna: And that’s a different use case. Personalized pricing is what Amazon was criticized for years ago. Lucas: Right. Dynamic pricing based on time or inventory is generally accepted. Personalized pricing based on your purchase history or location — that’s where you get into potential discrimination. No major fintech wants to go there yet, but the technology exists. Luna: Speaking of technology, what’s the AI piece? Is it just simple rules, or is there machine learning at work? Lucas: The more advanced players use reinforcement learning. The algorithm tries a price, sees the demand response, and learns over time. It’s not just if-then rules — it’s predicting elasticity in real time. For example, if a rainstorm hits, does demand for hot soup go up or down? The model learns that pattern. Luna: That’s impressive. But it also means the system can make mistakes. What happens if it misprices and alienates customers? Lucas: That’s why most implementations have human oversight. The restaurant owner sets guardrails — never above $X, never below $Y — and the algorithm operates within those. And they can override any price manually. The fintechs also provide dashboards showing how often the algorithm changed prices and the impact on sales. Luna: Before we wrap, I want to ask about the competitive landscape. Who are the key fintechs in this space? Lucas: You’ve got Sauce Pricing, which I mentioned, focused on restaurants. Then there’s Juice — no relation to the pizza chain — which does dynamic pricing for retail and e-commerce. Wiser is another, more focused on competitor price tracking. And the big POS players are building in-house. Luna: And these are all fintechs because they’re integrated with payments — they see the transaction data. Lucas: Exactly. Without the payment data, you’re just guessing. That’s the fintech advantage. Luna: Lucas, I’ve been thinking — this kind of content, free and ad-free, only exists because listeners support it. If you’ve found value in these 100 episodes, a couple of dollars a month is genuinely what keeps these going — buy me a coffee dot com slash fexingo, if you’ve gotten something out of them. Lucas: Yeah, I’ll echo that. We keep the show independent because of that support — no sponsors, no agenda. Just the topics we think matter. Luna: And every dollar helps. So if you can, that’s buy me a coffee dot com slash fexingo. Lucas: Alright — back to the pricing frontier. One area I think will explode is dynamic pricing for services — think hair salons, auto repair. A fintech called Boulevard, which does booking and payments for salons, is already testing it. Luna: So if I book a haircut at 10 AM on a Tuesday, I pay less than Saturday at 3 PM? That seems logical. Lucas: Exactly. And the fintech handles the payment side, so the price is locked in at booking. That reduces no-shows too — because the customer has a financial incentive to pick a time that’s cheaper. Luna: Interesting. What about the next five years — where does this go? Lucas: I think we’ll see more integration with loyalty programs. If you’re a regular, you might get access to lower dynamic prices — a sort of subscription discount. And fintechs will start using the pricing data to offer working capital loans — if your restaurant is raising prices, you’re probably busy, so you might need inventory financing. Luna: So the pricing engine becomes a signal for other financial products. That’s a whole new layer. Lucas: Exactly. And that’s the beauty of fintech — the transaction data is a lens into the business’s health. Dynamic pricing is just one application, but it’s a powerful one. Luna: Alright, episode 100 in the books. Thanks for the conversation, Lucas. Lucas: Thanks, Luna. Here’s to the next hundred.