The Price is Smart – Unlocking Profits with AI-Powered Pricing

All right. So, welcome everyone. Thank you for joining us today for this special session on pricing innovation in retail. My name is Barak. I'm the VP growth here at Quick Lizard and I will be your host for today. So, today um we wanted to cut through the AI noise. So, really what does AI powered pricing really mean for us retailers in 2025? uh and how can we really drive real measurable business value? I have today with me uh two fantastic colleagues. I have Matt Schiffman who's our VP of pricing strategy in the US. He brings years of experience at BCG and now works with us on our most forwardthinking retailers state side. On the other side, I have Egonal who's our product manager here at Quick Lizard for many years. He leads the development of our nextg pricing engines that are really designed for scale and agility.

So, uh, right before we kick off, uh, quick note, our Q&A session is open throughout. Um, and we have our AI pricing experts online and ready to answer any of your questions in real time. Please don't hesitate to drop any of your thoughts or ask anything as we go. We will surely answer them. If you don't mind, gentlemen, let's dive right in. So, AIdriven pricing, let's start with the big picture question. There's been a lot of noise around AI pricing and AI in general. Matt, in your take and what you've seen in the past few years, what does AIdriven pricing actually mean in 2025 and is it just hype? Thanks Barack and um hello everyone. So I I'm very excited to be part of the webinar. Um when we talk about AIdriven pricing, you know, pricing has always been an analytically sophisticated domain. When I was in grad school, I was studying econometrics at Chicago.

No one called it data science or AI or even machine learning back then. It was econometrics but that's been a part of the field for for decades. Um we we when we talk about AI pricing people usually mean a mixture of what at this point is called classical machine learning and So these days you need transparency. You need machine learning to action to figure out what will happen with a price change. But Genai is the new thing. It's the the much more exciting thing. Um, and it's progressing incredibly quickly. It allows for more responsiveness, faster cycles. Um, but you have to be very careful with um, using Genai in enterprises anywhere, but especially in pricing. Most Geni pilots fail. I was reading a study the other day that something like 3% of pilots are put into production. So, you have to be very, very tactical around where you're using Genai and how to make sure it sticks.

Um, one example from a a case I I was working with a client. They were a convenience store. They had done a whole big Genai like strategy visioning project. They thought to themselves like, "Okay, here's the future of Genai." I show up um 9 months later, I come up with a very simple use case of Geni, which was literally just understanding some scrape data, interpreting it, um, using chat GPT essentially. And that was the first real use case that actually entered production. And it's those tactical use cases that we're seeing within um the world of Genai and pricing that are actually sticking. It's using it for BI. It's using for explaining decisions. It's using it for um speeding up implementation. It's often fixing the product catalog. It's those tactical little things that start to build out a more robust pricing function that are really moving the needle in 2025.

So, we're really seeing, you know, from your your graduate days the move from theoretical to much more of the practical and really starting in the tactical level um with these retailers. Super interesting. Um, Eagle. So, from a product standpoint um what's under the hood? So, how does that actually look like and what makes a pricing engine truly AI powered? >> Thanks, Rack. Uh, also happy to join and thank you for having me. Um so from my perspective as a product manager here uh an AI pricing engine is about making the right call at the right moment. You constantly read signals sales trends inventory levels competitor competition movements market events etc. The main idea is to seal it or or act before the window closes. Pricing with AI is tougher than any in other use cases because every change is public. It affects margins instantly instantly faces constant market scrutiny.

That's why models must adapt in real time, not just act as a static script. Although uh speed and accuracy matter, trust does too. So retailers need to understand why a price changed, not just see the final uh bottom line. And if you blindly follow AI, you might boost profits for the short term, but you might damage your perception and market position in the long run. So in my perspective, AI should enable your pricing strategy, not be the strategy. to point a use case. Looking back at my earlier days here at Quick Lizard, uh a customer of ours used one of our most uh powerful modules called competitor sensitivity, which in a nutshell provides an output of whether or not a competitor price affects our sales or your sales. The customer using this module strategically achieved a 45% increase in profit simply because his benchmark was now the relevant competition and not the entire market.

So again this enabled them to actually uh use these AI modules and change their prices without uh disrupting their uh market decision. Okay, super interesting guys. So I think you know kind of to sum up I would say so a beginning of AI pricing starting to creep in pricing strategies really using it on a tactical level um and really trying to gain the customers trust before we roll out it to be the entire strategy. Amazing. Um so Matt uh going back to you in terms of the pricing architecture for US retailers and I think you know that's kind of your bread and butter. So you work closely with American retailers. um what are most of them doing today in terms of their pricing maturity? What needs to change? And kind of where are they situated right now? >> That's a great question, Barack. So, broadly speaking, I would describe it as with big ambitions but trouble actioning.

we're going to see common gaps that that's that's true throughout every enterprise um that's considering anything with data which is legacy systems, siloed data, slow decision cycles um often trying to almost get ahead of themselves. I had a client for example they they were they were in the the apparel um space they were trying to build these cutting edge machine learning models. It was for a promo engine, but key data existed only on a vendor's third party ecom database, not accessible to anyone. Um, the they they were they were essentially trying to build a model on noise because the data they needed was in a system they didn't access. And they were basing this all off of a hightouch process that involved dozens of arcane spreadsheets getting passed around a team.

And ultimately, you know, they they they were unable to to realize their ambition until they fixed all those core um legacy problems more broadly. Um in terms of pricing maturity, you know, it's been a crazy um half decade. You you start with the the the COVID increases. We got waves of inflation, rising interest rates, more recent tariffs. So, the upside of this, at least from a pricing perspective, is there's been a lot of price changes. consumers expect them more but are also um getting angrier with what we're see um what with what they see in the market.

Um it means that pricing the execs are ready for pricing to be a core component of margin strategy to be a key strategic layer but it means it needs to be more than just a quarterly category um in some of the more sophisticated um companies that I've worked with, they're starting to think about experimentation and targeted strategies. Uh you you saw a lot of personalization in marketing over the last decade, but more recently, you're starting to see some of that in in the pricing space, especially more across the assortment versus across different customer bases. So think about strategies like varying prices by channel, having uh value based pricing in parts of your assortment, competitive focused pricing strategies, value based pricing strategies, costbased pricing strategies across the assortment um to to to better realize the overall strategy with this more targeted micro strategies.

Now at the same time, you've got to be careful. This leads to more governance, more cross cross functional alignment needs and generally uh increased need for sophistication to meet market demand. >> Perfect. So I think that also perfectly aligns with your previous answer right we're moving from a theoretical into a practical execs are ready for pricing to be at the forefront but the question is now how do they actually implement that and eagle I'll ask you that. So what challenges do us retailers face today in implementing these solutions? So integration is usually the first hurdle. Connecting a pricing solution um to an ERP or ecom systems uh can be challenging and requires some data alignment. Then there's also what I've mentioned before the trust. Shifting from human set prices to machine recommendations can feel risky.

So we often start with a sidebyside comparison aka AB testing uh to build confidence before going fully automated. Also there's category complexity which adds another layer of uh some challenge um because you face different uh behaviors in grocery, fashion, electronics. Each of these verticals behave totally different. Uh also change management is a huge thing because you need to make sure the teams get full value from the tech. Not always easy to change. Uh and in the US specifically promotions drive much of the pricing strategy. So we designed our AI to work handinhand with aggressive promo calendars instead of chasing them. >> Amazing. Um, super interesting. So again, alignment on the issues and how do we actually roll that out into the broader scope and the broader catalog of products.

So Matt, I want to take you back a couple steps and and talk a bit more strategically about the tariffs. So uh, everyone's talking about tariffs today. they're are coming in hot and fast and I think some retailers are are having issues on how to actually deal with them. Um so how should they approach these tariffs strategically? Is dynamic pricing a option for helping them mitigate that impact? Yeah, I mean Brock, you said some retailers, I think it's even every retailer, at least in the US, is struggling with tariffs and even abroad, they're the US tariffs are impacting the price and the cost of goods up and down the supply chain. You know, they're increasing um landed costs. They're eroding margin. Um they they're increasing upstream cost. So even for made in the USA goods, the ingredients of those goods can then impact the um the overall cost.

And the question then becomes, you know, what do you do with your price when your costs are changing in such a dynamic manner? Um dynamic pricing is is key here that you you're you're going to want to be able to rebalance your assortment and be very strategic. Um there there can be multiple layers. The the data side is very very important here. you're you're going to want to understand what are your tariff heavy goods. Um that could be at a skew level. It could even be within shipments. If if tariffs are changing over time, some of them some of a single product is sourced from Vietnam and some from China. They're going to be facing different tariffs. So trying to understand the loaded impact at any point in time of tariffs, what you want to be passing on to your customers versus what you want to eat in terms of your own margin um is key.

layering in that um you want to understand what your competition is doing. If the competition is raising prices, um there there's a lot more leeway for you to either raise prices or potentially make a strategic shift and try and um buy some share, try um try and drive volume by undercutting a competitor as they're raising prices. Um, you need to be able to understand what are the how tariffs are going to impact your costs, how those costs are then going to um be passed on to consumers and what you want your ultimate prices to be at a very very flexible and dynamic thing um dynamic um speed frankly like you have to almost be following the news full time to understand what are the latest tariffs, how are they going to impact me today and so what should my prices be today. It's it means that as as I mentioned before, it can't just be a monthly a quarterly reset.

It has to be a dynamic >> I really hear that data that data p part burning, you know, in your gut uh when it comes out. Nice to hear. Um great. So, we talked about I think a little bit about where we are today. I wanted to shift gears a little bit and talk about, you know, the next 12 to 18 months. What does that look like in terms of AI pricing? So, besides shifting gears to the future, let's shift gears also internally. Okay. So, Eigal uh this question will be for you. What trends uh do you feel should retailers be tracking in terms of AI pricing in the next 12 to 18 months? >> So, I think that AI assistants or agents will soon be a part of every pricing dashboard and this so decision makers can uh you know ask a question get an instant clear datadriven answer.

Um so and which will enable them again not to just get an answer or see the results in a beautiful dashboard but actually take action like adjusting a price or scheduling a promotion or even changing the entire rule set. Um these agents will actually do that and you will just need to approve it and maybe in the future not even that. So I think we'll see more real-time fusion of competitive cost and demand data. um no more waiting for those weekly sessions uh and reports. Uh you'll have more no code environments and tools that will let you let the the business users to uh just you know run their own scenarios without depending on any IT and uh get the results like in a flash. So, Agentic AI, I think that's uh you know, that's a it's a term that we keep >> Uh it's a term we keep hearing and now to hear that uh creeping kind of its way back into pricing as well is super interesting.

Matt, um what do you what is your take on that? >> Yeah, I think that anyone involved in technology and AI is excited where it's going to go. um within the next 12 to 18 months, you know, AI is going to continue to improve. The enterprise applications are going to become more readily available. Um and you know, their their costs are going to continue to go down. I think layering in kind of where where where they can start to apply, it's piloting agents to assist with that that that mixture of like the strategic layer, the machine learning layer, the decision-making layer. Um it for many many retailers they have teams um prices but focused on the KBIs. They're focused on the most important SKUs that drive the most business that drive the customer's perception. Those are crucial. Those are by definition the most important SKs.

But as we uh as AI starts to become more sophisticated and these agents become capable of taking more action, the the long tail is the first place to look towards them. you you want to be able to in the future give them as much attention as KBIs, learn from, you know, how you're handling that portion of the portfolio and start applying their own strategic judgment to the long tail, which right now for many retailers is if not being ignored, at least not being not the main Um, we want to be able to directly translate strategic concerns into actionable pricing strategy. You know, I've I've had clients where they they warn me, you know, the CEO might on a Friday afternoon come in and say, "Hey, Monday, you know, we want to change our entire pricing strategy or really the entire company strategy to be more around generating um inventory turn versus bottom line impact.

you know come up with a plan for that right now and that translating the big level strategy which AI is is not that's not strong suit to a tactical plan um can be a great use of agents. We're also going to be seeing lower cost of AI um and using that for things like AB testing promotional strategies. It's really just whenever there's something that is, you know, you've been able to do yourself, but with the main focus the over the next 12 to 18 months, it's starting to experiment with how you could let an agent manage that for the parts that there's not the capacity to right now. >> So, pricing strategy equals company strategy. I like that. I think that's a that's a good kind of it's a good headwind. Um, I want to touch about something else you talked about.

you talk about customer perception and and I think it's it's quite the hot potato um today and and I think that leads us to to our next version. I would say the US in particular the US consumers are now more skeptical than ever. Uh if they feel a lot of the retailers are hiking prices uh even if margins are tight they still see that as unfair. How can pricing strategy really address this company consumer gap? Are there any tactics that you're familiar with um from your vast, you know, history of of working with these types of clients that will help improve that perception uh while still protecting uh the company >> Yeah, it's a tough place to be right now. Like rising prices are on the consumer's mind. You know, you hear political pundits talking about how, you know, this past election was decided on the price of eggs. What this means is price perception is key here.

Um you need to be smart about I mentioned earlier the KBIS. What are your KBIs? What are the items that drive price perception and understanding that within your catalog? Um this needs to be a mixture of um AI um intelligence, business judgment. Figure that out. Um, and crucially here, you you you want to be able to then understand what this price perception means for your portfolio. The you don't want to um just raise prices on on KBIs. I've seen retailers try and do this. I I I had a client once um they they had put together an elasticity model. It was trying to predict, you know, what you should be doing. And surprisingly enough, this was in 2023.

you you just come off of COVID um price hikes, demand surges and the elasticity model told them you you know you should just raise your price on every single product um that's going to increase your profit and mathematically like that would have been true like if they had done that they would have spent um had a great quarter but at the same time eroded trust with customers and ruined their business over the next year. Um it it really comes to um when they were trying to figure out what to do identifying I talked earlier about the long tail. What are those SKs that are less salient to people's mind that have more room to move um and that you know can um where you can move prices as those those costs are increasing where you can pass things on without damaging your own price perception. Um this can even vary um product to product and competitor to competitor.

So what I mean by that is sometimes you'll have a product where um your competition is relevant to it where they're they're moving prices. It's safer to move your own price. Um because everyone in the market is sometimes it doesn't matter at all what your competition is doing that you're known for that product. That's your thing. you need to be very careful even if your competitors are moving it uh their prices. So what it comes down to is you know I we we're I talk about AI. I warn about AI but you need to be very smart with AI to understand where it's safe to move prices. Typically it's going to be the long tail. It's going to be items that are um where your competition where you're sensitive to your competition and their moving prices. Um, and that's where you have to maintain your margin while potentially sacrificing it on those, you know, KBIs that are driving price perception.

>> Interesting. So, really taking again, I think taking it from the exact level of those sort of the blanket price hikes and really just taking it down to a more tactical approach and really looking at it from a consumer perspective without eroding your business or your margins. Interesting. Um, EAD, this next one is for you. um pricing and shopper data. Um we see across the world and I think especially in the US these retailers and brands sit on mountains raw data mountain but they're still struggling to make these fast decisions. How do you feel like this vertical can turn raw data into action without really overwhelming the business So what we do when we onboard a customer we focus on three main pillars. One is structure, second is accessibility and So the first thing we want to do is create a single source of truth from all these fragments of data.

Uh in the end you have multiple uh areas to collect data from and you need to organize them in one uh useful structure. Then we give the teams tools to act on it quickly. So uh we call it pricing triggers like spotting a competitor price drop or a low inventory or maybe even a high spike in demand etc. And then that allows you again to adjust these um uh pricing rules uh quickly and not wait for like a daily cycle or maybe even a weekly The last thing and which is the clarity which is highly important for many of our customers um if not all of them is making the reasoning reasoning visible uh and easy to understand. So features like our uh decision tree that shows you step by step what exactly happened to that specific uh product uh al along with other builtin uh widgets and custom widgets which we can also create for a specific user.

um allows these pricing managers to understand both the number I mean like the bottom line of the um recommendation but also the logic behind I I I heard triggers and and I think for me triggers is something that I love because I feel like that is an overarching theme for any type of raw data that you have and I think that allows you to I would say clear the clutter right and utilize only triggers that you feel that are relevant for your type of business whether it's in my fields or or both of yours pricing uh Matt I see you kind of raring to go so I'll I'll let you have a stab here um data mountains of data how can you help that uh or the teams that you're working with create fast decisions Yeah, the at BCG we used to talk about a concept called 102070 that the business value came 10% from the algorithms, 20% from the data and 70% from the people's and process.

Um the triggers is a great great example of that. What what matters is not um what the what certainly what the algorithm is telling you or what the data is saying. What matters is what you actually do about it. So it's making sure that you understand um if say let's go back to tariff there's a tariff that's coming through um okay so when you uh how will it impact you how will it affect you and what are you going to do about it it's making sure you have the processes that are built on the data and the intelligence that you're seeing um to turn um the just the the the mountain of data into actions.

Um you having having the right intelligence is essential there and then having the organizational capacity the strategic judgment to then say okay here's our strategy when we see this number or this action here is the process that we're going to do and here's the decision that we are going to make to change something to change a price um to to change change our position to um this is what um the actual impact of it will That's how you're going to be able to handle the data, how you're going to be able to handle um the the complexity of the modern, you know, business >> Love it. 10 2070 everyone. Um love it. Okay, so before we jump into uh this next section, I see some comments um being made on the chat. Great guys. Um pile them on. Uh we'll take them as we go and I think also Matt and you'll have some time to answer these in the end. Let's play a fun game, gentlemen.

Um, let's play start, stop, double down. So, if you were a retail pricing or a category exec uh watching us right now, um, what's what's one thing you would start, something you would stop, and something you will double down on today in this ecosystem? Uh, Egon, let's start with you. >> Start making more datadriven rather than gut feeling uh, pricing decision in real time. Stop relying solely on your gut system, your instincts. uh you're losing money guaranteed. Uh and double down. I'd say scale up the relevant AI pricing modules and sync them with promos and assortment. >> Super cool. Uh I like this game. Matt, your two cents here? >> Sure. Um in terms of start, you know, I'm sure we have a lot of pricing professionals in the audience. Um make sure you're thinking strategically. View your role as essential to core strategy. defining pricing's role in your company's strategy.

Um whether that's to drive traffic, to improve brand position, to improve cash flow, think of yourself as part of the strategy team, not just a technical function. In terms of stop, um I would say, you know, uh stop making decisions on gut feel. Try to make sure that you're basing it off of um hard data that you can then directly link um to the the core strategy. um to to drive the business value. Um and then in terms of doubling down, you know, you need transparency. Um you need machine learning and AI that can help you make those decisions. So start doubling down on where where the AI can move the needle um towards your strategic concerns um and focus um focus the the technical efforts there. >> Okay. Um great. I'll add mine. No one asked me, but I'll add mine. I think you start moving from the strategical to the tactical in the world of AI pricing.

I think you need to stop at the moment to being afraid of AI pricing and really start to embrace it on a tactical level of course with trust and the correct providers and I will double down on the data. Uh I think you know God is in the details and the data is where you should find it and I think EAL both of you mentioned the triggers. So don't drown in data. Um find the triggers that are relevant for you and double down on them. Um, great. So, we have some questions for the chat, as I just noted. Um, here's one for Matt. Matt, a lot of us here are dealing with inflationdriven pricing sensitivity. How can retailers raise prices where needed without damaging long-term customer trust? >> Yeah, that's a great question. And, you know, when my answer earlier with the tariffs, I think a lot of that applies here. It's understanding what items drive price perception.

It's understanding what your competitors are doing. It's having the data to action on it and moving prices strategically. It's also taking the customer on the journey with you. you know, whether it's experimenting with showing why um if tariffs are driving the the price increases, if there's costs going up. Um it's being transparent, it's negotiating with suppliers um to try um try and protect, you know, keep your cost down, splitting splitting the tariff costs with your suppliers. Um but ultimately what it has to come down to is um understanding what is driving price um and being careful there about maybe losing some margin in some areas and trading it for margin in other areas. >> Interesting. All right. Great answer. Great question. Um Egon uh we have one for you. So Egon, you mentioned integration with ERP and POS systems.

who super technical as a challenge for a retailer starting this journey today. What would you feel like is the practical first step to get AI pricing connected without having major disruption as much as possible with with those systems? >> I'd say generally the first thing a retailer should do when they're, you know, getting interested in using a pricing solution is getting their data structured and ready for digestion. It may seem trivial, but we've seen it often times with retailers. This part can be one of the biggest challenges as their data again is scattered in various systems each focusing on different aspects of their business. So reviewing the data truly understanding what can actually move the needle would take you a long way. >> All right. Um so that was questions from the audience. Uh Matt, do you have any kind of closing statements uh before we kind of wrap up?

Just kind of oneliner. Yeah, I guess the theme here that um of what I've been saying is that pricing is a core strategic lever um and that AI is the future of how to action on it at scale. So just constantly be thinking about you know what is my company's strategy? how um what are the new tools that are emerging on the market um that that that use AI that can help me um increase my own leverage that can increase my team's capacity um and that can let me make decisions that move the needle faster. >> Yep. I think that's it's a really it's a really good summary. Um so again um we're going to wrap up here. Thank you Matt. Thank you Eigel. Uh thank you everyone watching and participating on the sidelines. Uh we hope this started to shed some light on how AI powered pricing is evolving, how to think about it strategically towards a tactical and operational in your business.

Um this session is quite a commitment to us helping retailers lead through smarter pricing around the world. Stay tuned for more insights. Feel free to connect with any of us on LinkedIn. Until next time, thank you very much. Bye everyone.