Well, hello and thank you all again for tuning into another episode of the Professional Pricing Society podcast. My name is Terrence. In today's discussion, we have a very special guest with us. His name is Kieran Gontgay and he is here to discuss with us his new online course with PPS titled Using Analytics for AI for Pricing.
Kieran is the Director and founder of global launch base and Inter nationalized consulting firm founded in 2021, which helps European companies with international go to market strategies and market launches. Kieran also is the CEO and Founder of Rapid Pricer, which is an AI based company in Amsterdam that specializes in reducing food waste through real time pricing for retailers. Kieran, how are we doing today? Very good, very good. Thank you for the introduction, Terence. Absolutely.
Now you have a new course with us and we're super excited to kind of start promoting it and kind of getting more people to talk about it, which is the using analytics and AI for pricing. And you know, you are what some may consider an expert in the AI industry. And so we're super glad to have you. I want to jump into this conversation and ask you a few questions to kind of get give people a better grasp as to what they can expect with this new course and what are some of your
favorite things about it? But first foremost, how is analytics in AI currently being used in pricing today and how do you think it's changed in recent times? And this can probably provide a better context of what your course is kind of made-up of. Yeah. So analytics and AI, just to take it a few years ago, Terence, this was a very highly specialized skill. You would have to get people with the particular skill sets
to do it for you. Now with more tools available and people are able to use analytics and AI with the business knowledge, more of a business knowledge and less of a technical knowledge. Earlier it was the other way around. You needed more technical and little business skills to get something done to build a dashboard say for example, or build an algorithm which can give you pricing or to find out what is the right price based on
all the changing conditions. Now you could use many functionalities of tools in a do it yourself fashion. So it's important to know what is possible and what tools to use. But it's no longer necessary to know coding or you know, development or or writing all of your analytical algorithms. All of that is done automatically through algorithms which are pre built. So that's the big difference literally happening over the last couple of years and then pricing as well as many other
industries as well. Understanding basically how to utilize the tool of AI compared to in previous times where you have to go through all the coding, you know, jargon, if you will, and, and that skill set, but that's cool. And as it pertains to pricing, I'm sure there's a plethora of opportunities where AI can be utilized. And you know, we hear a lot of terms in industry like artificial intelligence, Gen. AILOM and and how relevant are they for the industry in the realm of pricing?
So, so it's very important to understand where we stand in the, in the ladder of using analytics and AI, right? So say for example, I have been to many a situation where the clients want to use the whatever the latest buzzword is, OK, the boardroom says we need to do something with Gen. AI. I'm like, OK, what have you started doing your rules based pricing? Do you know where your competition stands right now,
right. Have you done the basic infrastructure needed to start implementing all of the analytics and AI? So, so it is possible that the many, many say for example, when it comes to retail, the many innovative leading edge retailers like Target and Best Buy or Fantastics or even in Europe, the very small retailers, highly specialized, highly automated systems and they're putting to good use all of the latest technologies
available. But it's also important to know you might use the latest technologies to do simple tasks also. Let's say for example, you could feed multiple sources of data and say to Jane AI, if you build it right, what are the latest trends you see? Or give me the best set of prices and products I can use to promote for the upcoming Christmas season.
Given what's happened in the same situation last year, you could simply write a query now and bring out some very simple recommendations based on the latest technologies. But still, you're not going to have to do something like fully automate my prices based on the freshness of the produce kind of a situation yet. So very basic steps can use the most complex of AI if you have the right infrastructure in
place. But at the same time, you can't just say bring me Jane AI if you haven't done the basics of Excel and analytical infrastructure first, so. It sounds like there's tears to this and that levels, if you will, the the basics of the infrastructure of AI is essentially the foundation. It then once that's established, then we can start talking about other things like G and AI, things of that nature. Is that correct? Yeah, I, I, I wouldn't say talk about G and AI yet.
Let it have a background, see see what can be done with the tools you have already available to answer the business questions first. I thought, do we know what roles each categories are playing? Do we know which ones are my profit drivers? Right? Do we know which items are contributing to my share in market share growth or decline? Say, for example, where are my customers most sensitive to price changes? Don't worry so much about whether it is Gen.
AI or Excel in the background. Business needs to answer these questions first and then go on making it more and more efficient with more technology in the future. Got you. And staying obviously in the realm of pricing as it pertains to understanding the business behind it and using AI, can you walk us through how how the science works behind a typical price optimization solution?
This could be done using Excel, it could be done using like regression on on like something like SAS or something, or it could be done using AI, right? But the basic is you need to have the relevant input data going into your data lake or wherever you put it. It could be as simple as clean point of sale data, inventory information, sales information, holiday calendar, weather, demographics, economic conditions, whatever is relevant for you. Let's start with the basics first.
Once you have this data in place, we need to align this with what is the business question we're looking to solve with this available data, right? It's, it's good to have this cool data and you can easily build lots of visualization. What happened last year, last month where we're going up and down, but now we can put together algorithms and systems in place to make use of the data in real time.
Say, for example, if you're automatically overstocked on a certain product and there isn't enough time to sell it before the end of the season, right? That should automatically in your process of pricing come back and say, OK, we need to sell it at this price to make sure we sell this through, right? So build the right input infrastructure, have the right business rules in place to make sure you're making the right decision.
And then when you come out with an output, you can make the whole process into something which is streamlined, which happens automatically on an ongoing basis. So, so this course I have explained what could be some of
the sources of data. And again, you could start doing this with small tables in Excel, but you could do it a lot faster, clean up data a lot faster using AI, say, for example, or you could aggregate data or connect different sources of data using AI. But the basic framework needs to be understood from the scratch. What exactly is happening? You know, to give you a quick side example, that one of the best bosses I've ever had was the CEO of Fry's Electronics.
And he once told me that Kiran, go get me an algorithm built to reduce my inventory. OK, So I went and bought all these fancy books, and here's the algorithm. Give you an equation. It's going to save you $50 million. OK. Now, why is this a square root? Why is this not, you know, a square? Why are we doing the differentiation here? So he wanted to know every component of the equation, what it did, why we had it in there before we made it into an algorithm like that.
How we do it can come later. What are we doing? What are we trying to solve? What does each component of the data do for you? That needs to be understood without all of the technology, right? What is possible needs to be clear. And then we make it faster, right? Let's first go walking, and then let's take a bicycle, then a motorcycle, then a speedboat, whatever you want. That happens with technology later.
But this course explains what are the fundamentals you need to understand before you bring all of the technology on top of this. That's good. That's a, that's a, that's a great. And you know, that's a great point as well, because you want to understand everything as best you possibly can so that your inputs, what your your infrastructure is for this technology can be correct as far as it, you know, as it pertains to whatever the goal is the company is moving toward.
And so I'm glad you kind of, you know, broke it down and put it that way, because a lot of folks may just want to jump straight into the technology without having the, the true grasp of what their company needs, where the company is going and you know, their, their their company goals using tools like, you know, Excel or AI. So that's cool. Now let me ask you this as well. What are some techniques and processes that can be put in place to help with pricing using
analytics and AI? You kind of alluded to a little bit before, but if you don't mind going a bit in depth with that. So, so first thing is to make sure all of the data is in a centralized place and it is able to talk to each other, right? So say, for example, if the promotions team operate differently, the inventories coming from the warehouse, right?
The product assortment is with the category managers, the, the data needs to be in a place where we can connect all of the different streams of data, right? So, so once this data is available, I, I like to use this example of vegetables being chopped up before a chef can come in, right? We have all the ingredients lined up, we have all the meats and vegetables ready to go. Then you can think about how best are you going to use this to, to make your business decision.
So the infrastructure, the technology infrastructure, again, it's a lot easier these days and many, many organizations pretty much already have it in place. But we might want to specify that, hey, I need to have POS data connected to inventory, to the promotions data, say, for example, or to the store opening data. And then you'll have to go through answering your business questions one step at a time. OK, how are we doing against
competition? A simple elasticity analysis, say, for example, you could do it yourself or get it done. You can use those numbers of elasticity to find out what roles each products are playing or what role each category is playing, right? And then try to see which competitor matters to you by how much. You might just think Walmart, say, for example, might assume Target is the biggest competitor, but most likely it's it's going to be a store right across the street from Walmart.
It could be enabled open mom and pop store. So that shows up in the analysis once you have it tied up. And we ask the right question the right way, right? So the good news is there's always a good next step in this journey of analytics. You cannot say that I have figured out everything there is in this industry right You you could start as much as a simple Excel pivot table.
Go all the way up to using JNAI to automatically price your products based on whatever is happening around the store and inside the store. And you know, you are someone who is well versed in the market. What are some successful cases of using analytics and AI that you've seen in the market? So often times if you go to a really large solution provider, right?
So, so they have evolved their solutions so much and it has become so robust, it is actually detrimental that it, it loses the flexibility to change with the new needs of the market to the new data sources available, right? So, So what I have seen in the most successful case studies are almost always re engineering of a new algorithm based on the
needs of the business. And once these businesses figure out, OK, now we're going to start using, let's say for example, we're going to start using what kind of a traffic is parked in the parking lot to determine what kind of promotions I'm going to run inside the store. You could use JNAI to do that today. It can analyse what models of car are there, how big are the cars and who's likely to walk into the store and how many of them, and then tie it with all of the other factors to
determine the promotions, right? So, so I don't think I can give you direct names of my clients without permission. But if you see examples of what Best Buy is doing, what Target is doing in the US from a retail standpoint, you'll see many examples of pricing decisions or Amazon, Everybody's familiar with Amazon, right? So Amazon changes its prices of it's every single product on average changes its price in 10 minutes. And no person is making this
decision, right? They're basing it based on so many conditions and they are at a stage where nobody knows why these price changes are happening anymore. It is coming out of a black box, but it is what the results are getting demonstrated automatically. These are some end price use cases you would see.
You'll also see an edge. You'll see companies like what I do at Rapid Pricer. There is also our competitors who look at the condition of fresh produce and change the prices based on the how many days of life is left on it. And this is all using AI because you're you're recognizing those images and predicting demand in real time. And then in some cases, we're able to reflect these prices back immediately when retailers have electronic shelf labels.
And it reminds me of AI. Can't remember what the quote is or who said it, but the gist of that quote was if you don't
evolve, you'll get left behind. And the companies that tend to be the most successful are the ones that evolve as time progresses, as the trends happen, as you know, especially as it pertains to their specific market, their specific company, the clients and customers that they cater to, you know, tracking those very minute details, the type of car that typically comes into the parking lot, that that is information that is, I mean, extremely advanced compared to previous
years, because that way you can kind of decipher, you know, who should we start to market to a little bit better? And so that's, that's a high level marketing that a lot of these AI tools are, are really moving toward to better suit
their customers. I think of other companies like bigger name companies like Netflix, you know, if you're, if you watch television or if you watch movies online, they are really good at promoting and marketing specific things that you've watched that you tend to like because you've watched other things before, you know, and that's a huge market strategy, which is awesome to me. Agree, agree. See see one one more thing I want to put caution to hear Terrance.
It's, it's, it's very important to be innovative and to evolve, but easiest. And the first thing we should do is we should already learn from what other people have discovered in the market, right? Similar, which is why I love PPS and many courses on PPS. We should talk about how other people have evolved already. And then once, once you reach that level of maturity and then there's so much to learn from everybody else and then we can think about pushing the
boundaries. I like to use another quote is don't be the bleeding edge of technology, but try and be at the leading edge of technology. Don't innovate so much that you're experimenting with a lot of things. There's much to use which has already been experimented, and you can do a little bit more experimentation on top of it also. That's good.
Now, let me ask you this final question for those pricers who may be on the fence about taking your course or you know, not sure if it's not sure if it's worth their time, what would you say to those individuals that may be on the fence? I would say that it's a good idea for for, for you to check the book which I've written, which is called the expert guide to retail pricing load of the concepts from the book.
It's, it's in fact a support material for this course will be very relevant to what is being taught on the course. So that will give them a feel of what is going to be taught. And and if it does make sense, if it is, say for example, not everybody's in the same level of learning. Somebody might be a little bit too at the beginning, somebody might be looking at to learn advanced machine learning
algorithms. So you'll be able to find out where you fit and if this course is indeed the right fit for you based on the information you'll find on this book. It's available on Amazon or other places that could give them the edge. And then this podcast hopefully will give you some more information to make this decision also. So thank you for that, Terence. Absolutely not, not a problem. Thank you so much again for your time. Kieran.
Where can those who are interested in learning more about you and your company and what you stand for find out that information? The easiest, if you remember my name, it's Kiran gangway.com and you have a website. Otherwise my company is Rapid pricer.com. So these two places will be good. And then of course, we have our PPS material available. You're going to post these links too. Yes, absolutely. All right. Well, thanks so much again for your time, Karen.
Until next time. We'll see you all later. Have a good one. Bye. Bye. Thank you, Terence.