October 14, 2024
I’ve been immersed in the Amazon ecosystem for years, watching it evolve from my early days helping my parents bring their vacuum business online to launching my own successful ventures.
What fascinates me most is how pricing on Amazon has transformed—it’s no longer about being the cheapest, but about being the smartest.
On this episode of the Velocity Sellers podcast, I share insights on Profasee, the dynamic pricing tool I’ve developed that revolutionizes seller approaches much like Uber’s surge pricing model.
By implementing hyper-learning models that continuously adapt (similar to what you experience with Netflix or Instagram recommendations), we’ve helped countless sellers unlock hidden profit opportunities with minimal effort.
The data speaks for itself: our clients typically see 10-15% profit increases within just two months through strategic daily price adjustments.
Working alongside our data science and customer success teams, we ensure complete compliance with Amazon’s platform while maximizing both profitability and sales velocity—just ask Mina Elias at MMA Nutrition who transformed his business using our dynamic rotational pricing approach.
The future of Amazon success lies in harmonizing pricing with ad spend, moving beyond static approaches to embrace data-driven strategies that significantly enhance enterprise value.
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Full Transcript
Chad Rubin: Foreign.
Peter Sims: Welcome back to another episode of our Velocity Sellers podcast. I am your host, Velocity Pete, and today I am joined by a very esteemed and integral member of the Amazon community, Chad Rubin. Chad, thank you so much for joining me today to talk about a variety of different things, especially of course about Prophecy, but also just looking ahead at some future ideas for Amazon and really just where the whole community and the whole company is headed. So again, thank you so much for joining me today.
Chad Rubin: Yeah, thanks for having me. Excited to be here.
Peter Sims: Thank you. Yeah, we’re excited to have you. I’d love to start out by— if anyone out there that’s watching that somehow doesn’t know who you are or what you do, I’d love if you can give a quick overview of your experience with Amazon and kind of just your background.
Chad Rubin: Great. Yeah. Seventeen years in the Amazon game. Have the hair loss under here to prove it. Started off with an e-commerce business. So my parents owned a vacuum store when I was growing up. I helped them get their business online. I then moved to private label. This is 2007, 2008. My account manager was a guy named James Thompson. He left Amazon and then we started with two other people. The Prosper Show is the world’s largest Amazon seller conference. We sold that in 2019. I started an inventory software for big Amazon brands and Shopify brands called Skubana. We sold that in 2021, and now I’ve been building Prophecy since 2021, which is like Uber surge pricing for Amazon brands. So we essentially will take the customer’s willingness to buy with the value that you’re offering in the marketplace, make sure that it’s being met, and capitalize on any mispricing around the platform to exploit it for profitability.
Peter Sims: Love it. Yeah, well, thank you for that. I’m excited to get into that with you. I am—not really ashamed to admit that I know very little about the topic of repricing and really managing that and making sure that it’s on top of everything. But I am excited to go over it all with you and take a look at everything. So yeah, let’s get—we can just go right into it. Give us a high-level overview of what your tool does and why it’s so important for resellers and anyone that can use the tool to really take advantage of it and how they, like you said, could be maybe missing out on some profit.
Chad Rubin: Yeah. So first things first is, like, we’re in a new era on Amazon where resellers have actually become a very small part of the population. We focus on private label, so we don’t live and die by the buy box. We live and die by the search engine ranking page on Amazon. So most companies just post their price on Amazon. They maybe looked at their competitor; that competitor maybe copied another competitor who was probably broke, and that’s how they price their product, and then they leave it there, no matter if there’s higher demand or less demand for that product or no matter if a competitor changes their product or something else changes—another signal. So what Prophecy does is we actually make sure that you’re pushing the perfect price at any given time on Amazon to maximize the intention that you have. So you might want to maximize profit without sacrificing or hurting your ranking position, you might want to maximize revenue, or you might want to maximize your velocity. So we’ll do all those different things with various different customizations using AI in the background. So there’s so many different data points to pull on on Amazon. We will pull those in—signals like your competitors, their price, their inventory position, your inventory position, your ad spend, your ranking position, your price, your sell-through, your top of share—and will essentially make sure that you’re pushing the right price at the right time to make you the biggest outcome possible on the platform.
Peter Sims: Wow. No, I love it.
Chad Rubin: Yeah.
Peter Sims: And it sounds like it’s, again, very complex. I’m sure there’s a big algorithm that goes into it. I’d love to hear more about the—you listed off a couple metrics there. What’s going into each one? Like, what is something that you look for in each of those that really tells you that you do have this best price and that your—your—your clients and people that you work with are—are staying on top of it?
Chad Rubin: Yeah. So we collect—so when you connect your—you connect your Seller Central account via API with one click, it’s a two-minute integration, and your Amazon Advertising account. And so that enables us to collect and to enrich and then to act on. We’re talking about millions of critical data points. So we go back two years on Amazon Seller Central. So we’re looking at your seasonality, we’re looking at your holidays, we’re looking at Prime Day, we’re looking at other calendar events that were important. We’re looking at your sales trends, we’re looking at your sessions, your impressions on Amazon per product detail page, conversion rate, ad spend, BSR, your click-through rate—all of this is ingested, and then on top of that, we pull in your competitors. So for every ASIN, we pull in ten competitors, their price point, their—their BSR, their ranking position, et cetera. So we crunch all of this to really uncover opportunities in the dataset that are always overlooked by brands. Most brands just statically price their product. But the next step, I think, is even more critical. So most brands never change price. So it’s like, imagine if you go on Netflix, you’ve never watched a video—the—the engine, the algorithm wouldn’t know what to serve you. Are you watching Cocomelon, or are you going to watch—I don’t know—Love Is Blind—or—or something—something else, right?
Peter Sims: One of those reality shows, right?
Chad Rubin: Yeah, exactly. So—but the—that—or Instagram, if you never like anything, or Facebook, if you never comment on anything, it doesn’t know what to—doesn’t know how to hit your dopamine.
Peter Sims: Yeah.
Chad Rubin: And so what we do is we do something called hyper-learning. And hopefully, I’m not going into too much detail here.
Peter Sims: I love all the details, so keep going.
Chad Rubin: We essentially force the model to price-test at scale different changes within a specific boundary of a negative 5 to plus 5% boundary to understand: how does Amazon.com react to those price changes, how do your customers react to those price changes, and how do your competitors react to those price changes?
Peter Sims: And why—why that number? That number, the plus or minus 5?
Chad Rubin: We’re just making small changes—like methodical changes—to see and gather precise data so that we can make even better predictions and optimizations with a stronger foundation. Because you can’t really make a change on Amazon without actually seeing: how do customers react, how do competitors react, how does Amazon react? So we have this, like, testing phase that we call hyper-learning, where we train the algorithm, and we explore different price points and understand what’s happened, which better informs our model to even make better decisions.
Peter Sims: Wow, this—it makes sense, you know, it checks out. I was just curious if that’s—like, why wouldn’t that be, like, 1—you know—1%—or—or something even, like, a larger change? Like, would that really not do as much? Like, you said you want that precise measurement. So are you sticking to that small number for that reason, or—
Chad Rubin: It’s within this negative 5 to plus 5% range. This is driven based on our data science team and based on our learnings over the past four years. But if you wanted a more technical response, I’m not your guy.
Peter Sims: Got it. Yeah, I figured that was—there’s some deeper, you know—like—like you said, more technical answer there, but it sounds like it’s working. It sounds like you’re getting a good idea of—you know—driving—like I said—hyper-training this model to kind of catch up. I’m curious, after the hyper-training and after you’ve kind of got that range established, what’s next? What do you do? What are you really looking at? And how are you identifying how to help your partners and your customers?
Chad Rubin: So after that thirty days, we move right into the optimization phase. So this is where we take all that data, and we put it to work. We deploy the perfect price to uncover the mispricing opportunity—the pricing inefficiencies that are a source of profit on the platform. And we rinse, wash, and repeat those. And so what may seem to be, like, minor anomalies can actually generate a significant amount of money for our brands. On average, we see a 10 to 15% lift in profit in the first two months of the brands that we work with.
Peter Sims: Wow. And that’s just from—
Chad Rubin: Sometimes it’s 20, sometimes it’s 30, sometimes 40, but on average, it’s 10%.
Peter Sims: Yeah. And I’m curious—and I have a question, of course, I want to ask you as we sort of get out of the process, but I want to hear the full thing. So after those thirty days, now you’ve got your optimization phase. How long do you—I guess—how long do you implement a new price? Or how long do you identify—like—okay, you could be—maybe—you know—after you find out what they maybe missed out on, and then you reprice them a little bit, or you change the price—what—what’s the—is it just continuously doing that to see—okay—well, we tried this new price—maybe it didn’t work—maybe it definitely did work—and then from there, it’s just smooth sailing? What’s—what’s the sort of—I guess—the phase two of—of the tool?
Chad Rubin: So—well, firstly, we make changes once a day. Now, there are some brands that we work with—for example, there’s a bedbug brand that’s featured on our site, and they got—somehow, somebody on YouTube did a huge thing around bedbugs and shouted their company out. And we have a—we have a—there’s a feature on our platform called surge pricing. And what that means is we do a check for demand throughout the day—so intraday checks of demand. And imagine, somehow, you starting all this demand to your listing, and you’re just leaving your price stagnant or static—you don’t capitalize on that. So typically, we change price once a day, but for specific brands, we also have an umbrella brand that we also are changing price more than—sometimes one time a day—based on demand that comes into the platform. So once we change pricing once a day, we do this for thirty days. We have a results call where our data science team and our customer success team hops on the call with our clients to look at the results together—to show them the great things that we’re doing, how much more money we’ve made them, or—like—if they’ve—if they’re not trying to optimize for profit, how much more velocity that they’ve driven on the specific ASINs. And we have all these different analysis methods in our platform that help those sellers to see the results and to show how we’re being more effective on their account.
Peter Sims: Wow. I did not think it was going to be sometimes even more than once a day. I thought it was—I thought you were talking here, like, once or twice a week you were adjusting these prices. I only asked that because when I—back—way back in the day when I was a brand manager—to do a price increase was a little bit—and maybe you know as well—to do a price increase, it’s a little bit more complex where you can’t just—you know—just change it. And maybe you might have some consequences from Amazon—a little bit of backlash. So are you guys making—and it sounds like you’re making, just, again—these incremental—incremental price changes. Have you—and I guess you haven’t because you’re still in business—but what does Amazon think about this? Like, do they—have they ever said anything in terms of, like—hey—careful—you raise your price too much—or something like that?
Chad Rubin: Yep. So Amazon—we’re actually a partner in their app store. So for Amazon, Amazon’s intention is, like—hey—I want you to always be the lowest price, no matter what.
Peter Sims: Yeah.
Chad Rubin: And we’re creating an opportunity where you don’t always have to be the lowest price. And I—by the way—I’m happy to, like—share with you—I have, like, graphs I can share if—like—if you guys are wanting me to, I can definitely share that. The other thing I just wanted to mention that you said was around—you thought price changes maybe once a week or so. The interesting thing is, like—there might be a category or ASIN where you have more shoppers that convert more—or more demand—that happens on the weekend or on a Monday or a Tuesday. So it’s not enough to just change price once a week. That’s an inefficient way of pricing. We’re doing it on a daily basis.
Peter Sims: It makes sense. Like I said, when you explain it, it just makes sense—you know—like I said—you could be missing out—you could be missing out on—like you said—profitability-wise. Maybe you could be charging slightly more, maybe you could be charging slightly less—get a little more sales in there. It’s very interesting, and the science behind it is very—I guess it’s a really other website—it’s interesting—it’s very cool, really, to hear about it from—from your side of things. So yeah—no—I’d love to hear that—I’d love to see some graphs and some examples if you have them. I know there is a shared screen option on our—on our tool here.
Chad Rubin: So yeah—I’d love to—let me share.
Peter Sims: There it is. Perfect.
Chad Rubin: So—so, like I said, most brands that are using Prophecy are making, on average, 10% in the first two months, and we have a Prophecy guarantee. So it’s a no-brainer. We have a guarantee of a 3x ROI, or we keep working for free until we deliver the ROI. So let’s just use—this is an influencer in the space. He also happens to have a supplements company—may recognize his name—Mina Elias—and he runs a supplement company, MMA Nutrition. And so first, we’re gonna actually look at his catalog for a second. You can see—and you see my screen, right?
Peter Sims: Yes.
Chad Rubin: The red line is profit of this year, and the blue line is profit of last year, and the bottom is our pricing. And you can see, like, when—you can see when he’s activated Prophecy and when there’s a gap, really, that doesn’t follow and bucks the trend of what was happening historically. The gap between the red and the blue line are immediate areas of gain that we had with a specific product. Now, as we started doing dynamic rotational pricing for him—so changing price on a daily basis—we started feeling that there was more demand. So as he entered into a higher—higher demand season—we essentially started to surge pricing. And that surge, as you can see, created a wider gap between the red and the blue lines. And that gap, again, starts even spreading further as we increase price and felt like there was more demand to be captured. Now, this is on his tablet, but let’s look at his product for a second, shall we? Because this is really where it gets super interesting. So I’m going to go to all time, and I’m going to look at his price changes. So you can see, like, most sellers very rarely changing price—sometimes doing a deal—a lightning deal, perhaps—but for the most part, it’s just flat and static. Now, the green line here is his ranking position. Now, look at what happened when we took over. We started doing dynamic rotational pricing. His ranking drops in a great way—right—the lower the ranking, the better. And so now we’ve got more demand than ever before. And we’re essentially testing different price levels—going up on different days, going down on different days—all right? This is all spread out over a course of time. And then we started seeing that there was even more demand. So we surge-priced here, and BSR is flat. Most people say—wait a minute—my BSR is going to get so impacted if I change price. It’s not always the case—right—you have to let data drive those decisions. And so you can see here—raise price—and then we most recently raised price again. And this gap—this opportunity—we’re talking about a seven—this is a seven-figure profit capture.
Peter Sims: Wow—no—wow. I mean, the data is there, the science proves it. I mean, this is definitely—and again, I think those concerns that you just expressed about—maybe some people would say—I don’t want to lose BSR—I don’t want to affect that in any way, shape, or form—but I mean—it’s the numbers—the numbers are there.
Chad Rubin: So here’s another guy—his name is Mike Jackness. He also runs a podcast—Ecom Crew—and he has a course, and so he’s done a lot—he’s—he’s an OG in the space—done a lot of stuff in the space. So we took over his brand, and he actually was able to sell his brand after we increased a good amount of enterprise value for him. So using the same analysis I just showed you—this is an example of where we actually lowered price in the off-season. So we lowered the price—BSR actually was able to increase—then, as demand started to increase during the in-season, we started to raise price and surge it over time. And you can see the gap again—right—where we made this nice spread where we captured profit at every opportunity.
Peter Sims: Wow.
Chad Rubin: Yeah, it’s pretty—pretty amazing.
Peter Sims: The seasonal products too, I’m sure, can benefit from this more—more so than even the all-year-round products, I’m sure—you know—just staying on top of that and making sure that there’s not—you know—you’re not missing out on anything, or you’re not overpricing and staying just connected with—with your kind of—I guess, some sort of, like, your customers and your—your sales base and making sure that they’re not—again—you’re not missing out on a proper profit or opportunity. This is very—very—very cool—very—very cool tool you got here. So yeah—no—that’s—that’s—thank you for that—for sharing that presentation and those numbers to kind of back it up and—and show us all the—the data and the science behind it.
Chad Rubin: Totally. And before Prophecy—right—when I was running my own e-commerce business—doing it manually—there would be this huge spreadsheet that I built, which is—I call it the Pricing Framework Spreadsheet. And this Pricing Framework Spreadsheet has, like, every input that I thought that would want to include. And to analyze this—I’ll just share it real quick—you’d have to go through each ASIN—so every tab would be its own ASIN—you’d set up your goal for it, and then you have to say—okay—like—do I want to—what’s happening—like—I make a change—what happens—what happened to BSR—what happened to profit—what happened to profit lift—ad spend—COGS—what happened to sessions—conversion—keep going—right. So I essentially was managing this massive spreadsheet tracking all these different inputs on 500 private-label products, and it’s just not scalable. It’s not even scalable to do it on one product because you don’t know if you’re making the right decision. And the only way to know is to actually put this into a decision-making framework, like AI, where it can actually calculate and create its own if-then statements on an—like—umbrella of if-then statements that essentially get compiled over time to make way better decisions than you can be making manually.
Peter Sims: Wow. I mean, it’s very thorough. I love it. I love to see how—you know—this really—how it works, honestly, and really—like—again—just the inner mechanisms that make it tick and make your customers more profitable and make this a viable strategy for people to use. Kind of—speaking of that—I’d love to hear more about the—aside from, of course, just collecting profit—what are the other important points that a customer—potential customer of yours—should focus on when considering using Prophecy? What else can it help them achieve?
Chad Rubin: Yeah, I think for me, it’s, like—showing you is probably the best approach. If you don’t mind, I can just keep showing you. So first thing you do is you connect your account—takes two minutes. You connect your Seller Central account and your Advertising account, and now you’re at a point where you can set up your SKUs. So a few things is that you can pick your goal. So Amazon’s algorithm is very sensitive to pricing, and that’s why you need AI—you need a supercharged pattern recognition system that can actually take these inputs and react in real-time to those inputs. So you have increasing profit as one goal you can set up—you may want to increase revenue, or you may want to target a specific velocity using price. And then you can set up customizations or configurations around profile. So you might want to be more aggressive, or you can be risk-averse or neutral. You might have a Subscribe and Save. And so each of these selections dictates how our model reacts. So if you’re Subscribe and Save, our model tends to be way more neutral and cap the upside of your max price—of your ceiling price—so that you can’t go further up. If you have a bestseller badge, we’re also going to be very risk-averse. Then you can say—okay—do I want to maximize my gross profit—my net profit? So gross profit is before your ad expense, net profit is after ads—and none is, like—hey—I don’t care about profit—I just want to rank. Then you can put your min price and max price bounds here. So you give us boundaries that you want us to operate within.
Peter Sims: Got it.
Chad Rubin: And then you put your cost of goods sold in. Now, if you have variations—whether they’re small, medium, large, extra-large—or perhaps even color variations—you can essentially tell the model whether you want it to be strictly synced together—meaning you want the prices to all work together—or you want them to work asynchronously—but one is the lead—like, your hero spotlight SKU that Amazon focuses on when you search a specific keyword becomes the lead, and the other SKUs follow in tandem with that specifically. So you can create variation kits and bundles in the platform. A lot of other customizations here—but I’m just gonna—for—for the sake of time and not talking your ear off so much—you can enable inventory intelligence and surge pricing and target velocity numbers. And—and I just want to point out that we are a software—but we’re a software with a service. So we essentially provide customer success tools and insights where, like—you’re not alone in this process of getting set up. And then we go into—okay—so this is how much money you’ve made over the past 621 days with Prophecy—this is what we’ve delivered to you in the past 23 days—this is the average profit lift on a monthly basis. And then we scroll down, and then we can start looking in and showing you real-time graphs around—hey—like—based on this strategy that you’ve set up around profit—this is what happened with your catalog—or this is what happened with your specific ASIN for revenue as we took over versus the benchmark—that benchmark forecast that we create before you start on the platform. We have an activity timeline that shows you audit trail history and, like—what percentage of that—we don’t need to manage all the SKUs in the account—so we can show you what percentage of the SKUs we are managing versus not managing. And then you can see just different notifications around what happened to price performance on a—like—did you run out of stock—did Amazon suppress your listing—but what happened with your specific SKUs? And you can also sort this by ASIN, by parent, by group, etc. And then we can show you a P&L of what happened this time versus last year and also this time versus the forecast. So we’re really digging deep into, like—with data—showing you what happened but also showing you—hey—here’s the—here’s the opportunity for improvement—this is what’s happening with ad spend—and this is why, perhaps, price and ad spend should be harmonized together to unlock even more nonlinear profits than you thought were possible.
Peter Sims: Wow. Yeah, it’s really in-depth, and again—I kind of—I expected this a little bit to have this much kind of overview and management on the tool—but this is really—really—really cool, and don’t worry about talking my ear off—this is all very interesting stuff. So anything—anything you’re willing to show me, I’m willing to listen to and to learn about. So this tool is very—very well set up.
Chad Rubin: Thank you—thank you so much. We put a lot of time into it. I’m trying to just keep it simple and actionable for you and your audience—so perfect.
Peter Sims: And I know—I would hope and think that the audience appreciates that as well. So yeah—no—this is—this is—this is great. And I have a couple more questions just about the tool itself and kind of what you guys do in any different situations and maybe if you have any crazy—crazy stories about a client that you can share—you know—with—without—you know—revealing too much about them—where they used your tool and something crazy happened—or a very unique situation surrounding it. But really, I’d love to hear more—just stories about how you have seen success with this and maybe why this is somewhat—I don’t want to say overlooked—but why so many people like to leave their price stagnant as opposed to maybe doing something like this.
Chad Rubin: Yeah—why don’t we start on that—I think that’s actually probably the best thing to start on—is that pricing is very easy to talk about. So you’ll have gurus out there—influencers—saying—just raise your price 10%—there’s inflation happening—and that’s not always the optimal decision—is actually much harder in practice—and especially on Amazon where—where you have this second order of consequence. So you have—if you change price—it affects your ranking position—your discoverability. And so you have to actually consume both of those data points, and they have to be very tightly pulled together so you’re making the right decision. So—like I mentioned earlier—most companies on Amazon are just copying their price—they’re just looking at their—their competitors—or they’re statically pricing their product. When they first launch the product, they’re saying—hey—look here—this is what everyone else is offering for—let’s copy the price of the brand that’s doing the best—and then maybe—or maybe go slightly below to remain competitive—and that person copied somebody else. But a lot of people also have other challenges around price—which is really just, like—fear—for example—is a big one. Misinformation—there’s a lot of misinformation out there—like—a lot of brands are trying to listen to hearsay versus actually doing their own testing—which is why we do hyper-learning. That hyper-learning phase—that stage that I referred to—is us uncovering, like—well—what hap—how does Amazon react for your specific ASIN when you make a price change? Because every ASIN is—is unique and different. And so a price change that’s way too high or out of line actually might create some suppression issues. And so we actually pull that into our model right away where if there was a suppression that happened—we reset back to the original price. And I think, like—the most—the biggest one probably is just inattention—like—people just don’t spend a lot of time thinking about their price—they spend a lot of time thinking about advertising.
Peter Sims: Yep—yep.
Chad Rubin: And by the way—who—who—like—who benefits from that—right? Who benefits from spending more on Amazon? It’s—it’s Amazon.com—yeah—Amazon doesn’t actually benefit when you actually make price changes—or very—like—in a very little way, perhaps—but there’s just not a lot of focus on strategic price changes on Amazon—no one’s talking about it—nobody’s done it—nobody’s been able to pull it off. And there is a better approach. And that better approach is to essentially make conscious and smart price adjustments and to equip yourself with knowledge on your ASINs—using your own data to drive pricing decisions so they can be made without fear.
Peter Sims: Yeah—and I think that’s a great point—again—something I hadn’t considered—that—you know—Amazon’s never going to tell you to lower your price—they don’t want you to do that—even if it would drive some more sales—you know—they’re still making less per item now—you know—technically speaking. So they definitely—I can—I can see that—it’s very interesting to kind of think about the—the marketing sort of—and the sort of what they push and what they don’t. But yeah—no—I—I—again—as a brand manager—and I was a very junior brand manager—but still—I—repricing was not—and—you know—changing the price and making it dynamic was not something on my radar—not something that I was—I don’t want to necessarily say taught—but it just wasn’t a big topic—you know—it just wasn’t something that you should be focusing on—you should focus on—how do you make your ads more profitable—are you A/B testing—are you—you know—checking out your different content—you know—are you using tools like PickFu to—to—you know—make better decisions—that kind of stuff—so quick little shout-out to John.
Chad Rubin: Yeah—and that’s—by the way—PickFu is a great software.
Peter Sims: Right.
Chad Rubin: I think PickFu is great, and I think optimizing your listing certainly can’t hurt—or sometimes could hurt—but—like—I think there’s just way too much effort and time and, like—all these other softwares that spend time around PPC and around keyword improvement—when you have pricing—which is the biggest—it’s, like—it’s the—it’s the smallest lever that swings the biggest door—and it doesn’t require you to spend more money—it just requires you to shake what your mama gave you. It’s really monetization of price, and it has the biggest impact, and nobody is doing it.
Peter Sims: Yeah—that’s a great phrase—the lever and the door—because—like I said—you think you just kind of—it’s overlooked—I feel like now I’m going to say it—I’m going to say—I think it’s overlooked—despite it being—I—I would think that with reviews—it’s probably one of the biggest driving factors—right—and I’m guessing that’s what you meant by your lever and door—more so than anything else—it’s those two things together are going to—what’s going to drive your sales—and that’s how you stay in business—so definitely something to consider—and if—
Chad Rubin: Your audience doesn’t use Prophecy after this—that’s great—I think the—the biggest thing that perhaps they can get out of this—is spending more time on your pricing and asking yourself—well—how did we get to this price—and is this the right price—and what is the right price—and did we guess our price? And so just giving it a bigger seat at the table—because pricing touches all parts of an organization—and all parts of that org should touch pricing—so pricing should be a bigger conversation and get a bigger seat at the table in leadership meetings.
Peter Sims: Yeah—that’s—I—I would think at—you know—giant companies—that might be something more important—and if you—you know—want to sell like the giant companies—you’re going to have to act like them too—so—yeah—no—great—great—great points there. Well—I’d love to get into any kind of crazy examples or any kind of crazy stories that you might have with some—again—anything that you can share—or anything unique that happened with any kind of—any information that would be fun and kind of, like—an engaging—
Chad Rubin: I don’t have anything crazy—but that same idea—you—that case study—and we have so many case studies on our site—when you go to our site—let me just go to it real quick—or when I—even that example I shared with you—like—the BSR dropping as we’re doing dynamic rotational pricing with our AI—is a big one—right? That’s a counterintuitive example. We do have, like—videos on our site—we also have results all over our site with case studies of clients using Prophecy—how much money that they’ve made—nothing crazy—you know—there’s a crazy example here of this bedbug powder that went viral on a YouTube video—and, like—we were able to surge-price—he didn’t even know he went viral—it wasn’t a paid-for sponsorship—and, like—we drove a ton of money for this specific client—but yeah—there’s a whole bunch here—and we have a whole lot more that we’re working on building—and you can read the different case studies here—nothing, like—nothing too crazy—just us helping people make more money—and yeah—I think we built a cool mousetrap.
Peter Sims: Maybe there’s something to be said there about that—there’s nothing—it’s—it’s simple—you know—it’s just a simple kind of fix that—it’s—and to use your example—it’s a small lever that opens a very big door—so it doesn’t have to be flashy and doesn’t have to be fancy—it doesn’t have to be—you know—wild—crazy story—it’s just a simple concept that—again—is not—is not considered as much—but should be—I like your other analogy—give it—give it a bigger seat at the table—make it part of your discussions—your quarterly—your monthly discussions—for Prophecy—it’s a daily discussion—or a daily look at—you know—is your price accurate right now?
Chad Rubin: So I was just going to say, like—another interesting point—is that most of my career—the success came from these, like—nook-and-cranny ideas—right? This Prosper Show—like—there was no community for Amazon sellers at the time—and this is really—these nooks and crannies are where success happens—and it’s actually no different from when you look at pricing—right? Pricing is a game of fractions—so it’s small changes to price—but as you’re selling unit velocity on top of that—it builds up over time—and so I look at pricing that same way as just—it’s just a lever—it’s just a lever—it’s a lever to make more money.
Peter Sims: I love it—I love it—well—that was a good kind of—I would love to put that on a pillow and kind of end on that—but—are there any final thoughts—Chad—on—on really—about repricing and Prophecy in general?
Chad Rubin: Other thoughts? I mean—I definitely have a lot of thoughts—I have a pretty strong opinion about the world and Amazon specifically—but I do think that first-mover advantage is very important on Amazon—I think AI specifically can be applied to pricing—inventory—PPC—and listing optimization—and done better than what a human can do—and I think that those will be the sort of next-gen winners of the space—and I think over time—over time—Amazon becomes this sort of—the brands become algorithmic—where, like—the algorithm—just like Wall Street—is making these—like—the algorithms are making decisions far better—faster—and cheaper than what a human can do—and that’s what becomes of Amazon—it becomes just, like—Wall Street—a financial marketplace—or a marketplace of commodity goods where items are sold—and this is just part of—dynamic pricing is just one part of that algorithmic process.
Peter Sims: Yeah—and that kind of answers my—my—my real final question—which is kind of—what’s next? What’s next for—for Amazon—but also really—what’s next for—for you guys at Prophecy as well—and looking ahead—you know—at the time of this recording—we’re almost right into—into Quarter Four here—the busiest time of the year, of course—and I’d love to hear more—even if you have anything else—more about just—what are you guys looking ahead for—you know—what are you moving towards?
Chad Rubin: Well—one of the things—so we—we see pretty large lift with just pricing alone—and we’ve started to work with agencies and other brand managers around really unifying price and ad spend together—and price allows you to—if you change price—let’s just say you increase price—it allows you to actually open up margin opportunity where you can spend more—and I’m happy to share my screen and kind of go into this with you a bit—I know you thought this conversation was done—but it’s not.
Peter Sims: That’s how a lot of my conversations go—so I cannot be surprised.
Chad Rubin: So let’s just use this table here—so here’s the big—big unlock that we—we’ve really been spending a lot of time on—which is the idea that—if you increase price 20%—now—this is just an example—you now can increase your ACOS 20%—so we’ve increased ACOS and price 20%—and we’ve done something that was counterintuitive—because, like—I believe that ACOS is a bendable metric—a lot of people look at ACOS as the gospel—but it’s not—and what happens when you increase 20 and 20? I want you to look at the profit—profit has doubled—100%—so whereas your ROAS has decreased—and it looks worse—maybe your brand manager would be, like—upset if they see that their ACOS went up to 24%—or the ROAS went down—but your contribution profit dollars—if you’re focusing on the right numbers—contribution profit dollars went up—and so did POAS—which is your profit on your ad spend—increased dramatically to make more money for the business—so we’ve been giving agencies and brand managers this dynamic spreadsheet—let’s just say that every Monday—we know where pricing is going the week before—we tell them where pricing is going—and then they go and make their ad adjustments for either budget or high-performing keywords to align with where the pricing is going—to unlock even more dollars out of the business.
Peter Sims: Yeah—and like you said—people do think ACOS is—is the Bible—which is interesting to think that—you know—raising it can also just—can—can play a part in getting you some more profit and increasing your profitability overall—so yeah—this is a really cool tool—this is exciting—this—this—this is just in beta—
Chad Rubin: You said it’s more than beta—I mean—we’ve got probably—maybe, like—fifty clients using it right now—that—so—so pricing is already launched—right—pricing is done—but now this dynamic model that’s out there that essentially harmonizes price and ad spend together—that’s being given to our brands and doing—and we’re giving it to them so they can put it—put the—adjust their ad spend on their own.
Peter Sims: Wow—but—yeah—no—this is—this is a really—really good episode—I’m really glad that you had a chance to come on—and we got to talk about—you know—Prophecy—and really just how people could use it to make—to make their—you know—make their business better—make—you know—reach their goals—Chad—I want to—really want to—again—really want to thank you for coming on and being a part of this today and speaking to me and my audience and—again—just giving us some more information.
Chad Rubin: Yeah—good to have me on—and thanks for the opportunity.
Peter Sims: Yeah—of course—no problem—thanks, everybody, for watching—make sure to like and subscribe if you liked this episode today—or any of the episodes that we’ve watched—I’m gonna have Chad’s LinkedIn profile and Prophecy’s website in the YouTube description below—so if you’re in need of a good tool to help you—again—just be more profitable—or—again—reach your goals better on Amazon—check that out—and if there’s any other questions—you can comment on this video—or reach out to me or Chad directly—so—yeah—thanks, everybody, for watching—I’m Velocity Pete—and we will see you next week.