Alexander Shartsis is the co-founder and CEO of Perfect Price, an artificial intelligence (AI) company empowering companies to make better decisions about pricing, profitability, and utilization. He is an entrepreneur and executive with a background in business development, sales, product, and engineering. He is a member of the Forbes Technology Council. He wrote a book called The Ultimate Guide to Pricing Strategy: A Playbook for Behavior-Based Pricing and Promotions. Alex holds an MBA from the UCLA Anderson School of Management, an MSc from the London School of Economics, and an AB from Dartmouth College.
In this episode, Alexander shares his expertise in the dynamic pricing model, which industries use it, and will it be harder to use it on a subscription type of business over the traditional ones? You’ll also get to understand the concept of artificial intelligence and machine learning and how they are associated with pricing strategies. Further, learn how his company, Perfect Price, delivers artificial intelligence for pricing that will significantly impact your company’s revenue.
Why you have to check out today’s podcast:
- Discover the concept of dynamic pricing to help you keep pace with constantly changing market dynamics
- Learn how to maximize your business ‘profit with predictive analytics and powerful artificial intelligence (AI) pricing strategy
- Know the definition of artificial intelligence (AI) and machine learning
“Always put yourself in the customer’s shoes or just out of your own world of being a pricing professional and try and think about it differently. And maybe you’ll come to the same conclusion or maybe you’ll, you’ll have an epiphany and think about things totally and differently. Letting go of your assumptions and your conviction of being right about how you’re doing things today is a really powerful tool.”
– Alexander Shartsis
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01:32 – Alexander’s shares how his focus in bringing technologies got him into pricing
03:20 – The concept of dynamic pricing
05:08 – Car rentals and car manufacturers as examples of industries which use dynamic pricing
09:28 – Two ways to achieve dynamic pricing: demand-based pricing and market trend
12:25 – Gathering outside data to predict demand or willingness to pay
13:10 – The overlooked significance of a company’s own data – actual purchase data and web traffic data
17:24 – Example of an AI pricing strategy
18:20 – Definition of AI (artificial intelligence) and machine learning, and specifying Google Translate as a good example
22:34 – How Alex’s company, Perfect Price, provides data with the companies to get the results they want
25:23 – Difference of subscription business from the traditional types in terms of dynamic pricing
27:41 – A piece of pricing advice from Alex – “Be really open-minded about pricing. It’s really easy to get emotionally attached to the way you’ve been doing things.”
“It becomes much more about, how are you framing the problem? And I think the key difference with AI is learning from the data, as opposed to making a bunch of human understandable assumptions.” – Alexander Shartsis
“The thing that’s almost always overlooked by companies is their own data.” – Alexander Shartsis
“In our opinion, wherever you’re running an AI pricing strategy, you do need some human control over it.” – Alexander Shartsis
“It’s hard to do dynamic pricing for anything where the consumer or buyer really has a solid expectation of price. And that can be subscriptions.” – Alexander Shartsis
“It is so often that we start down some path for whatever reason. It’s easy as a startup company. We price it this way, and that’s mostly internally focused, not externally focused. And as soon as we step back and start thinking about customers and willingness to pay and how it changes in situations, suddenly, we have so many more doors, so many more levers we can pull.” – Mark Stiving
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Full Interview Transcript
(Note: This transcript was created using Temi, an AI transcription service. Please forgive any transcription or grammatical errors. We probably sounded better in real life.)
Alexander Shartsis: Always put yourself in the customer’s shoes or just out of your own world of being a pricing professional and try and think about it differently. And maybe you’ll come to the same conclusion or maybe you’ll, you’ll have an epiphany and think about things totally differently. Letting go of your assumptions and your conviction of being right about how you’re doing things today is a really powerful tool.
Mark Stiving: Welcome to Impact Pricing, the podcast where we discuss pricing, value and the amazing relationship between them. I’m Mark Stiving and today our guest is Alex Shartsis. Here are three things you want to know about Alex before we start. He is co-founder and CEO of Perfect Price, a company that uses artificial intelligence for price and revenue management and that’s what I’m especially excited to learn about today. He wrote a book called The Ultimate Guide to Pricing Strategy: A Playbook for Behavior-Based Pricing and Promotions, I’d bet a lot of that has to do with this company and Alex was invited to be a member of the Forbes Technology Council. What an amazing honor. Welcome, Alex.
Alexander Shartsis: Thanks for having me, Mark.
Mark Stiving: Now this is going to be fun. As I read through your Linkedin page, your background doesn’t scream pricing at me. Most people I talked to, it’s pricing, pricing, pricing. How did you get into pricing?
Alex Shartsis: That’s a great question. I think if you’d asked me when I graduated from college would I be running a pricing company, I would’ve looked at you sideways like you’re crazy. But I’ve had it. So I’ve worked for early stage technology companies my whole career and after business school, is been my focus is bringing new technologies to market and everywhere I’ve had to deal with really big pricing challenges and so I’ve been able to create a lot of value through pricing and when my co-founder and I got together and we’re talking about ideas to use the technology we were familiar with and bring them to market in a new way, pricing was just an obvious choice.
Mark Stiving: Oh, that’s pretty neat. You essentially said, look, I’ve had to deal with pricing my entire career. I have this really cool technology, what if we put them together and we can solve this amazing problem?
Alex Shartsis: Yeah, that’s exactly it. A lot of the challenge with pricing in many companies is, and I don’t want to overgeneralize, but I’m sure some of your listeners will empathize. It’s either a second thought or it’s people just lack the tools, right? They don’t know how to price the value because they don’t know what the value is. That’s what our technology was really good at figuring out, and so that’s why we thought this would be a great area to focus on.
Mark Stiving: Yeah, I think it’s actually a third or fourth thought. It’s certainly one that’s the last thought that goes through their mind before they launch a product most of the time. So, when you say the technology, I assume we’re talking AI
Alex Shartsis: AI has gotten a lot of marketing around it. We talk about it as AI and AI is a good way to think about it, but really fundamentally, what we do, the actual underlying technologies, a lot of supervised machine learning and other AI techniques and combined with a SAAS is software as a service delivery platform.
Mark Stiving: Oh, that’s pretty cool. Okay. I’m just, I’m getting goosebumps. I’m excited. But before I jump into the technical side, let’s just talk about this concept of dynamic pricing, because everything I read about your company, you’re pushing dynamic pricing. Is that the main piece of pricing you focus on? Is that just what your technology really helps well with?
Alex Shartsis: Yeah, so a bit of both. I think, I mean, if we were having this conversation say, 15 years from now and we’re looking back on 2019 or how people ran their companies in 2030 or 2040 you’re not using people to set up hundreds of rules to price things.
Speaker 1: You’re not downloading things into excel. Right? That just isn’t, if you think about the future, a lot of this is done by AI, by machines, just as it’s done today by some of the leading companies, Amazon, Uber, Lyft, Airbnb. There’s a huge list of innovative companies that are using AI to do pricing already. And I think if you think in the future, let’s look back. Yeah, I mean why? Why would you publish your price list in January? Like, I could just give me one good reason when everybody expects pricing to move around based on demand. So I’m sure there’ll be a few industries that hold out energy and Rolls Royce will still be selling jet engines based on, uh, January price list. But the vast majority of things, especially the consumers buy will be the price would just reflect demand. And as a result of that, we focus our technology on dynamic pricing because that’s what we think our customers have said that they really need.
Speaker 1: And that’s where we think the world is going. Where you know, sellers are, are gonna be for, for many things, not everything, but for many things where demand fluctuates and supply fluctuates, the consumer expects the price to reflect that fluctuation and the company’s much better served by capturing that value.
Mark Stiving: Can you just list a few examples of things that we would all be familiar with that say, Oh, here’s dynamic pricing. Obviously, surge pricing with Uber
Alex Shartsis: Yeah, surge pricing with Uber is a good example. I think another good example is car rentals. You know, everybody’s gone and rented a car, or many, many people have rented a car sitting in a hotel, but it goes deeper than that. So I mean, an example that really surprised me was a car manufacturer. So think about, do you enjoy buying a car? Right? Who enjoys going into a dealership and haggling and, uh, a lot of the car manufacturers are realizing that if they can accurately price the car, they could sell cars online, right? And so Tesla has been a big change agent in the car industry and other companies that have come to us and said, okay, well now we have global operation. We now need to centralize control pricing in say, Germany, right? Or Detroit, I don’t know what’s going on in Indonesia or Japan or all these other markets. But they’re all looking to corporate to figure out pricing globally. And it turns out pricing for cars changes day to day. Demand for cars changes seasonally. It changes based on a bunch of different factors. And using our technology, they can actually capture that, like understand the demand for their, for their products on a very granular basis. Around the world and then adjust prices accordingly in near real time so that when you take away that sales negotiation, you’re still able to get… Just capture the value from the customer. Right? You’re not going to sell out of cars in, you’re going to capture the value of that.
Mark Stiving: I gotta tell you, Alex, from a consumer perspective, I now hate you.
Alex Shartsis: Funny. I mean I, so I think that maybe we can all think Uber for that. I think the reality is that a lot of cars are… you, you’re wasting your time, right? The car, the dealership wants an extra 5,000 bucks for the car, 3000 bucks for the car. Cause that’s their margin more than it’s actually worth in the market. So they’re going to make you go in there and sit in an uncomfortable chair for the better part of a Saturday just to squeeze out the $2,000 and they’ll probably still got $500 at the car isn’t worth. Whereas if, say, you know, Toyota or Subaru and Nissan actually priced the car accurately, to begin with and you went in and for your market for that car, you get a price that’s actually a fair price. You know, it just, it makes it the best car buying experience better. And by the way, this was already happening. I mean, Carmax sells used cars with the no haggle price shift, which is a startup here in San Francisco basically sells cars online and there’s, and there’s no haggling. So you know, I mean there’s, there’s a precedent for it and consumers really like both of those companies have really happy customers and you go look at the Yelp reviews of your average Toyota dealership. They’re not too, not too positive.
Mark Stiving: I think it’s very fair that says you’re going to save me a Saturday, but what you are now causing me to do, just like I do when I buy airplane tickets, well, should I buy it on Tuesday or on Saturday? Should I buy it six weeks ahead or two weeks ahead? And now I’ve got to do the same thing when I buy a car?
Alex Shartsis: Well, I mean, I hate the regularity, but if you’re not doing that now, you’re losing a lot of money on buying a car. The price has already changed. You know, if you go in on the first of the month, they’re going to pay more than if you went on the 30th.
Mark Stiving: That’s a true statement.
Alex Shartsis: If you’re not doing that now you’re, you’re leaving even more money on the table then you wouldn’t be at. But we’re more dynamic.
Mark Stiving: You know, ignorance really is bliss at times.
Alex Shartsis: That’s what I mean. Don’t hate the player, hate the game.
Mark Stiving: Okay. So when we think about dynamic pricing, most examples I think of, um, are usually demand based. Is it ever something else? So if I were, um, you know, I do the Memorial Day sale at Macy’s. Is that dynamic pricing, the way you think of it or is that just we do a sale once in a while?
Alex Shartsis: The Memorial Day Sale is more of a promotion, I would say, than a dynamic price. Now, I would argue that Macy’s doesn’t have a lot of science behind what promotions they offer. They just think, you know, 40% off the traffic to the store and you could put a lot more science behind that. Our technology can actually do a lot in terms of simulating what will happen and giving people insights. The pricing team or the promotion seen insights into how effective something’s going to be. But I, to me, dynamic pricing is really everyday price. And so the two ways to get there, are demand-based pricing. So what is, what is this value to the buyer or the customer, you know, what is somebody willing to pay for a Toyota Camry with these features, you know, in Detroit or in whatever city that is. And then the other side of it is, well, what else is going on in the market? So, you know, there are times where knowing what your competition’s doing is important and you know, and that, I mean the rental car industry is a good example of that. There are times where even if people are willing to pay $30 a day to run a car from you in that particular location, if everybody else is running the same car for 15 you’re not going to rent that many cars for 30 you might run sound, but you’re not going to write that many and so it depends. Your dynamic price needs to take into account not only in demand-supply but also what else is going on in that market.
Mark Stiving: I can kind of think of dynamic pricing as having two sides to it. One side is what’s going on in the marketplace and so I’m trying to estimate your willingness to pay it’s a special event. Of course, you’re going to come to the Superbowl. you’re willing to pay more to be there. I get that. The other side is it isn’t necessarily that I’ve got people who are willing to pay more. I’m sorry that somebody is willing to pay more. It’s really the fact that I have this huge range of buyers and I only have so much supply. Therefore, I’m going to take the top set of buyers only and just sell to them. Does that make sense?
Alex Shartsis: That can be effective I think. I guess it’s good that you’re doing something and nothing in that in that example, but I think that what traditionally operations research methods have approached the revenue management. The traditional approach is having different buckets and saying, oh, well these are my business travelers, right? Those are my higher value customers and I’m gonna, you know, they’re going to the Superbowl and I’m going to charge them more because you know, I put them in this bucket.
Speaker 1: But the reality, especially in the travel industry is the lines have gotten really, really blurry. You’ve got people vacationing, coming back on a Saturday to save a little money on the flight maybe, or just because they have other obligations. You’ve got vacationers leaving on a Wednesday, which was traditionally not a day that people left for a vacation and then working from wherever they’re going. I know our team does that all the time just to save a little bit of money on the flight. So, I mean, it becomes, in that sense, it becomes much more about how are you framing the problem? And I think the Dif, the key difference with AI is learning from the data as opposed to making a bunch of human understandable assumptions. So if people are willing to pay more on Fridays, or willing to pay more on Wednesdays, or willing to pay more three weeks before that event, great, let’s, let’s charge more then. Let’s do that in the context of the whole event and how do we maximize our revenue from that event. But it’s less of trying to, trying to fit human understandable assumptions into, into, uh, into a price, arbitrary or otherwise. And so I’m not sure if that totally answers your question, but I think it’s a different way of looking at the problem.
Mark Stiving: Yes. And so you, you and your company, you gather a whole bunch of outside data. That’s not just the data that, you know, let’s talk about rental cars for a second, right? A rental car company obviously has tons of historical purchase data, but you’re going to take more data on top of that and say, oh, these are things that predict demand or willingness to pay for a rental car. Is that true?
Alex Shartsis: Yeah, so so absolutely. So there, they’re really a couple of key components of data that we look at and so one of them is absolutely about outside those outside factors and that could be the weather, it could be how many flights arrive, where those flights are coming from, you know, how many seats are on them, how full they are, right. There’s, there’s a lot of really interesting outside data that can be applied to this. But the thing that’s almost always overlooked by companies is their own data, right? You’ve got either your actual purchase data, you know, how many, how many bookings do you have already, or how many, how many transactions did you do in a, in a related period, 30 days ago or 28 days ago, last quarter, last year, whatever that right way to relate that as. The other side of it is, you know, if you have web traffic data, right? How many, how many people are searching for that on your website or how many inquiries are you getting from, from other, other sources, other agents? And, and a lot of the times, I mean not a lot of it times, almost every company we talked to is just throwing that data out, right? If you think about the conversion data, especially, you know, if your conversion rate on your website is one or 2%, which is good, especially for the travel industry, that means that you’re just throwing out 98 or 99% of your data.
Mark Stiving: And it never hit me that says we can use web search data as predictors of demand. But I mean it makes all the sense in the world. I just never thought of it.
Alex Shartsis: Well, and so, and I mean that’s, we thought because we came from the online ad industry and that’s what’s been really interesting is we just look at this problem differently. Another, another example, right? If you’re a B2B company selling through your own website, right? So you have maybe sales reps who go around, but you’re, your customer puts in order online, and this, this is increasingly the trend. I know Mckesson, which is the biggest pharmaceutical medical distributor. There are hospitals use a website to go order stuff, right? In the B2B contact. A lot of people worry about losing sales to competition, right? Well, how much of my customer’s wallet do I have versus leakage or however you want to put that right? They went to your website and they price something out and they didn’t buy it. You know that’s a sign that you don’t have if they’re talking on the phone, right? No, the sales rep is good enough to put in an order that didn’t happen, right? But if you filled out a form on the website that said, I want 15 of these and two of those and seven of those, right? And then you go do it on the competitor’s website. Maybe they give you a better price and you never, you never completed the purchase. That’s data that you can use that a lot of companies just drop on (inaudible) that.
Mark Stiving: Yeah, pretty fascinating. Pretty fast. Can I run a really weird dynamic pricing situation by, and maybe you can explain it to me.
Alex Shartsis: Sure. I’ll try.
Mark Stiving: It was probably five. I published a book in 2011 and it was probably four or five years ago.
Mark Stiving: Amazon had it for $250 or the book itself sold for 20 and I assumed because the publisher was out of stock. The reason for this was automatic pricing algorithms and what I ended up doing was putting the book on Amazon myself for 20 bucks and I just watched all those prices come back down towards my price. What was going on?
Alex Shartsis: Yeah, I think you’ve figured it out. So you know there are, so it could be a number of things. Again, this is highly speculative, but Amazon and if it was the third party seller, especially a lot of people use different software algorithms to, to price things on Amazon. I think one of the challenges with pricing, and you can run into this at any scale, and this might be what’s going on, is if you have a low expected, you know, if you’re, if you’re optimizing your price for expected value, so it’s, you know, the probability of a sale times the results of the sale, which is the price, you can optimize it into a really high price because you know, 0.1 times $1 million is actually a pretty big number.
Alex Shartsis: And so that may be what happened. You know, these things, they thought, well, there’s a lot of likelihood of a purchase, but if we get 320 bucks for it, that’s, you know, that’s good, you know, that we’d be happy with that and that’s the winning price versus say 20 bucks. And I think that you’re putting your book out there for $20 shows the importance of the market, right? Oh, suddenly there’s a different, there’s an alternative. Somebody could buy this for $20 maybe we should lower the price.
Mark Stiving: I diluted myself into thinking those were all people who had read the book and thought it was worth $250.
Alex Shartsis: I mean, it could’ve been that too. It depends on, depends on either the seller was, I think, you know, it’s a good example of why in our opinion, wherever you’re running an AI pricing strategy, you do need some human control over it. That said, you know, that’s like googling yourself, right? That’s your book. Whoever’s selling that, if it’s Amazon is selling literally millions of bucks, right? So you know, a lot of people, especially executives, they look at a pricing strategy. I get an example from our world. We work with a big guy, a rental company in New Zealand, and when I talked to their CEO, we rolled out the software. He was like, look, man, you know, Christmas Eve pricing just doesn’t look quite right in this day, in November. It doesn’t look quite right. I was like, oh, that’s, you know, we’ll look into that. What about everything else? He’s like, oh, the rest of it’s fine. Do you know? And so, so are you. I had gotten 363 days right. And it was just two days that weren’t quite right. And that’s what executives just go to the go to the problem. But if you get 363 days right in two days wrong here, you’re, you’re winning, right? And so you know your book, if it’s, if it’s mispriced, it’s just a good example of yes you need some oversight. But at the same time, you know, in the aggregate is that pricing strategy you’re winning. I think Amazon seems to be doing pretty well.
Mark Stiving: Oh absolutely. I just thought it was funny. So something I’ve never really known the answer to. And would you please help me? What is AI? What is machine learning?
Alex Shartsis: Great question. So, and this is a question we get a lot. So if you think about AI, artificial intelligence is about computers and machines. We know them. How are computers built with it, the phrase was coined it was machined being able to understand things for themselves without being explicitly told. And it’s very broad. So it’s everything from Google Search and deep learning to what we do around supervised machine learning and understanding demand. Um, it’s, there’s a thing called natural language processing where you have machines trying to understand the text, Google Translate is a good example where they’re trying to take things in Japanese and turn them into English without being told this word means dog, you know. So I think translation is a good example because it’s very tempting to say, oh well you, when you say, you know, dog and English, that means Chien in French or Perro in Spanish, right? That’s a role where the human said, hey, this word means that. And um, and what machine learning and artificial intelligence mean is that machine can figure out that perro and chien mean dog without ever being told that that’s what they mean. It can just look at enough examples and figure out that those two things mean the same. And I think when you think about it that way, when it’s like, oh, it’s, it’s able to raise the price on Saturday because demand is high on Saturday. Nobody told it to raise the price on Saturday. Nobody told it Saturdays were important. It just figured out that if it raises the price on Saturday, it’ll achieve its objective. That’s, that’s really what AI is. Machine learning is specifically is what I would describe as a subset of AI. And that’s a that’s where you’re training the machine. Either an unsupervised way where the machine just learns on its own or in a supervised way. And the supervised way is where you are. There’s more human involvement and you’re telling it, this is the objective you’re going for and you’re giving it a data set that has examples of success. So a good example of that is chess. When you’re, you know, the go was unsupervised machine learning through the Alpha supervised machine learning with chess is, hey, here are games that this person won, right? So that’s the outcome you want. Go look at all this data and figure out how to do that, right? That’s, that’s supervised machine learning approach.
Mark Stiving: So instead of teaching it the rules, you teach it to figure out the rules.
Alex Shartsis: Exactly. So we’ve used, so what people think, oh, well, the perfect place has an algorithm. We have a lot more than one algorithm, both the summer years at the same time. Some are used differently. But one of the things we do is try and figure out what is, what is the demand curve, right? What is the price in this city? What is somebody willing to pay? Again, without you having to say, oh, Saturdays people pay more. Right? We’ll figure that out if it’s in fact true. And we found a lot of companies for whatever reason, have actually gotten that wrong. Um, that they’ve done some research and they thought that they’ve figured out that there’s a premium associated with something, a product, a day, a time. It turns out that that just wasn’t there. And then the other thing that it can do is it can then learn from the strategies so it can learn, oh, if I raise price by this much, you know, so, so when you’re dealing with competitors, for example, people frequently think, okay, well if I just knew what my competitor was going to do, right? You know in that, and I’ll just tell the machine to raise the price by a dollar cause then my competitor won’t react or lower price by a dollar cause my competitor won’t react. And AI can actually learn what is the optimal strategy. Go try and compete against these people and try and figure out what the optimal strategy is and it’ll, it’ll figure it out.
Mark Stiving: And it may not be able to tell you how the competitors are going to respond but it has all that in its internal working. So we can say here’s the optimum decision for you to make today.
Alex Shartsis: Yeah, exactly. So I think, and that’s one of the big changes is you know a lot of companies we talked to, they really wanted to know why and why is a different question. You know you can, you can calculate a probability of a competitor response but that’s a different problem than winning or you know, whatever. Achieving your objective in the strategy, like understanding if there’s a 66% chance your competitor follows you if you move your price one way or the other doesn’t get included in the algorithm is trying to figure out how to, you know, win more market share or increase profitability.
Mark Stiving: Nice. Okay. So, um, this is going to sound like I’m letting you be a sales page, but I’m really, really, really curious. Let’s say that I am Hertz and I want to do business with Perfect Price. How do we do that?
Alex Shartsis: That’s a good question. So we’re not, not entirely hypothetical. So we’re a SAAS company, so basically at a high level do we, we operate on a, on an annual license basis. So just like Salesforce or any of the other SAAS companies, that means that there’s no setup or you know, there’s no, you don’t have to buy servers and put them in your company because some people still do that. And then a, and you don’t have to manage the software or you know, do anything with the software. We work with you to send your data to our system, which we make pretty straightforward and we work with your team or integrators to do that. And then you out your team as a way to log in and manage the prices and decide what to do. And then I could go into more detail about what people actually do in the software, but there’s a lot of functionality.
Mark Stiving: I think here is my real questions then are you going to come work with me and say, Oh let’s make sure we get your web search data. Let’s make sure we put in all of your historical purchase data. You’re going to help me say this is the data we know.
Alex Shartsis: Yes. So in the beginning of this, we go and find all that data with you and help you get it into the system in a way that’s, that’s usable and productive. And so we do that. And then yes, absolutely. That’s something that we brought to cause a lot of companies don’t even know where to start. Right? Like you were saying before, I didn’t even know my Webster’s data was useful. Right? Like, so they didn’t, you know, now, now we got to go get it and then it’s probably not in a format that’s all that useful to begin with. So we worked with them to get it in a format so that it can be put to good use and then you know, obviously we then enable them to push everything back into their system in an automated web.
Mark Stiving: Next thing is there’s the external data, third party data on, like Hertz, I’m not tracking weather, I’m not tracking at the Superbowl was in the city. There are all these things that go on externally. How do you say, do you sell pieces of that or do you just say, look, I use on everything that I have access to?
Alex Shartsis: Basically our model on the outside data front is we work with you if you have the data, or we’ll go get the data on your behalf and just manage it for you. There are a lot of providers or web data. We work with a few of them. So you know, depending on what problem you’re trying to solve, we’ll work with you to figure out which one’s the best one. We have events data on the platform already that we’ve either gone out and gotten, or we have other vendors who provide that and so basically whatever you need is already there ready to go. And it’s a question of just taking it in steps, right? Let’s start with the data that everybody thinks is the most valuable. Then let’s add, you know, incrementally more and more data to that so that you, you get the results that you want.
Mark Stiving: I’m geeking out, I’m loving this stuff.
Alex Shartsis: Yeah. Me too. It’s fun to just get to be my day job. Although, although some people think I’m a nerd.
Mark Stiving: Yeah, I wonder why does it matter to you? I don’t think it does. Does it matter to you if I’m a subscription business or a more traditional type of business?
Alex Shartsis: So it does a bit. And that’s because subscription businesses, just from a, from a technical standpoint, have a lot of trouble offering a lot of different subscription tiers. So I did pricing it, a couple of subscription businesses and it’s really challenging, but there’s this implicit challenge where you tried it in the same person’s credit card every month, right? So you can’t charge 30 bucks this month and 35 next month and 24 the month after that. What you can’t do obviously is test new subscribers in there. There are right and wrong ways to do that, but, but generally we don’t work with businesses that are purely a subscription business because it’s just a different type of pricing problem where, you know, and, and I mean, to be honest, if you were running a subscription business, we’d have, and you asked me for pricing advice, we’d have a conversation about offering more Goldilocks pricing, offering additional packages. You know, there’s a lot you can do with pricing that isn’t just changing that, that one product’s price. We work with companies itself, a good number of products, a lot of different ways. So, you know, you think about a hotel where you can rent any number of nights in a room across 20 different kinds of room across possibly thousands of properties. Right? That’s where our technology’s much better. Cause you may remember that you paid $200 a night to stay at a Hilton in Los Angeles the last time you were there. But when you, when you go this time, you fully expect not to get $200 a night again. You know, you’re not. Whereas if you, if you paid $40 for the Wall Street Journal last year, you expect to pay $40 this year and if it were suddenly something different, you might take note.
Mark Stiving: Yep. That’s absolutely true. And if I make the conclusion from what you just said, I think you said it is hard to do dynamic pricing for subscription companies.
Alex Shartsis: Yes. I think to generalize even further, it’s hard to do dynamic pricing for anything where the consumer or buyer really has a solid expectation of price. And so that can be subscriptions. It could also be milking, you know, if you buy milk every three days, you know what it costs and unless you can justify that price increase or decrease its dynamic pricing may not be, may not be right for that particular product.
Mark Stiving: Yep. Okay. That was brilliant. One more piece of brilliance. Um, we’ve got to wrap this up though. Alex, final question. What’s one piece of pricing advice you would give our listeners that you think would have a big impact on their business?
Alex Shartsis: Yeah, it’s a great question. You sent it to me before and I’ve been, I’ve been thinking about it for the last couple of days. To me, one of the things that I, I would really encourage people to do is to be really open-minded about their pricing, having priced my own company’s software, another company’s software as well as other products. I think it’s just really easy to get emotionally attached to the way you’ve been doing things. And I’m not, I’m not trying to sell you AI software here. I’m just trying to say like, think about always put yourself in the customer’s shoes or just out of your own world of being a pricing professional and trying to think about it differently. And maybe you’ll come to the same conclusion or maybe you’ll, you’ll have an epiphany and think about things totally differently. But we did that once and it was transformational for our business and we’ve seen other companies do that regardless of whether or not these use our software and it just let go of your assumptions and your conviction of being right about how you’re doing things today is a really powerful tool.
Mark Stiving: I think that was very well said. It is so often that we start down some path for whatever reason, it’s easy as a startup company we price it this way and that’s mostly internally focused, not externally focused. And as soon as we step back and start thinking about customers and willingness to pay and how it changes in situations, the boy suddenly week we have so many more doors, so many more levers we can pull.
Alex Shartsis: yeah, exactly.
Mark Stiving: Nicely done. Alex, thank you so much for your time today. If anyone wants to contact you, how can they do that?
Alex Shartsis: Well I’d be happy to hear from you. I’m on Linkedin, Alex Shartsis. There’s only one of me and also on Twitter @TheGeneralist and then our company, of course, you can reach me there. It’s perfectprice.com
Mark Stiving: Perfect and I will also have that in the show notes so people can find it easily. There went episode 21.
Alright. I learned a ton and I actually was able to organize a bunch of thoughts in my mind at least a little bit around dynamic pricing and AI. I have to say my favorite part was learning that I could have used web data to help me figure out prices, demand. How stupid, what was your favorite part?
Please let us know in the comments or wherever you downloaded and listened and while you’re at it, would you please give us a five-star review. We would greatly appreciate it. Also, if your B2B company needs to better understand subscription value and pricing. Check out our new course, although I hate bragging, I’m going to tell you it’s amazing.
Now, you may already know a lot about running a subscription business, but this course will give you valuable frameworks, new tactics, and even a pricing roadmap template. If you’d like to learn more about the course or have any suggestions or comments about the podcast, please drop me a note at email@example.com as always, don’t forget to listen next week to another episode of impact pricing.
Tags: pricing, value-based pricing