Optimizing Pricing with AI: Smarter Strategies for Foodservice Distribution

Pricing in the foodservice distribution industry is complex, but it doesn’t have to be overwhelming. Join us for a dynamic webinar that reveals how Smart Price Optimization can transform the way foodservice distributors, suppliers, and manufacturers manage pricing strategies.

Discover how leveraging AI pricing optimization can help your business maximize profitability, adapt to market changes, and eliminate guesswork in pricing. This session will reveal actionable strategies for optimizing price points, protecting margins, and enhancing customer satisfaction while maintaining efficient and aligned processes across your supply chain.

Whether you’re navigating fluctuating demand, juggling diverse product portfolios, or seeking better insights to guide pricing decisions, this webinar provides the clarity and tools you need to succeed.

Speakers

Full Transcript

Good afternoon. Welcome. For those of you who are joining us, I’ll give a few more minutes and let, everyone come in, but welcome.

Well, welcome, everyone. Appreciate you being here. Good afternoon on this Tuesday.

So well, good afternoon. My name is Carolyn Muller. I am the senior director of learning and development at IFDA, and we’re so delighted to have you join us today. This is a great, afternoon for a timely and strategic presentation, optimizing pricing with AI, start smarter strategies for food service distribution.

This session is, brought to you by our IFDA valued allied member PROS. So thank you so much for your support, of our organization and of of our members. Excuse me.

Before we start, I’ll do a couple quick housekeeping items.

For participation, you’ll notice that you came in, your microphones are off and things. That doesn’t mean we don’t want you to participate. So please, you’re muted for now. But if you have any questions, throw them in the chat. I’ll be monitoring them. We’ll do, as many as we can live. And if not, we’ll try to get to them at the end of the presentation today.

I’ll also note that this webinar is being recorded and will be available on the IFDA webinar section of our website, in the next few days. So if you see something that you have enjoyed, or that you would like to share with a colleague, please feel free to do that. You don’t have to have any other access to share that with someone. So, so let’s get started.

I’m pleased to introduce our, featured speaker, Daniel Wolf. He is the senior director of strategic consulting at PROS. He’s been with PROS for about fifteen years and brings extensive expertise in pricing and optimization and strategic consulting to help organizations navigate complex pricing challenges. So, before I turn it over, you know, our goal really is, you know, in the competitive food service landscape to, you know, bring you topics that will help, enhance your strategies.

We know how important this is, with the economy, with, you know, changes in consumer behavior. So we really feel this is a timely presentation.

And with that, Daniel, I’ll let you take the floor.

Awesome. Thank you very much. So, again, thanks for for joining today. So like like Carolyn mentioned, optimizing, using AI for food service distribution is what we’re gonna get into. So strategic consulting, that’s the team I run. So kinda think of that as your typical presales, team to support any, you know, pricing business use case and helping our prospects and customers understand how their business is a fit for optimization.

In the fifteen years I or almost fifteen years I’ve been at PROS, I’ve had a opportunity to work with many different food service distributors and manufacturers over the years.

Initially, on the solution delivery side, so implementation, I I help people configure the pro solution based off of their use cases. And then more recently on the the presale side, helping people really understand the impact of optimization and and how it can be a fit for their business. So what I really wanna do is take those captured learnings over the years and walk you through what I typically see as challenges and opportunities in the space, obviously focusing on on pricing technology. So like Carolyn said, questions, please ask away while we’re going. She’s gonna be monitoring the the q and a, and I’ll definitely save some, some time for questions at the end.

So on purpose, a blank slide here. So what is dynamic pricing in the food for food service distribution in this industry? So this is the million dollar question. This is what I get quite often in these conversations.

So I wanna I wanna start by talking about some ideas. I almost put a picture of a magic wand here because a lot of times people think of of this as being the magic wand and it really is to solve a lot of problems, but it’s how does it solve problems with your data, with your business constraints? How’s it how’s it fit in your industry? So I’ve got three statements I’m gonna go through.

So, we’ll we’ll be talking to customers, talking to prospects, and and one thing that they always talk about is, well, my suppliers are are constantly increasing my cost, constantly changing costs, and this, dynamic pricing engine is gonna be incredible and be able to react to those cost changes immediately. That probably sounds familiar to those on the phone, and then it immediately turns into a conversation about, but my suppliers don’t necessarily send me data at the same time, and it’s not in the same format.

And maybe my street business, which is a a large percentage of my business, I don’t wanna change those prices within a week because people are placing orders. We we hear that a lot. I’m I’m taking orders. I can’t have my price change all the time.

And so they might start to think, is is this too dynamic for me? One one customer said, it can’t be too dynamic because you don’t sit down at a restaurant and the waiter change the price while you’re ordering and change it again when you’re paying. That’s not how it works. So we we need to plan for these things.

So we’ll we’ll talk about how this works in in the industry and what dynamic really means and managing all those those cost changes.

The next one that we hear is my competition is undercutting me. I need to be way more dynamic. I’m gonna have this pricing engine that’s gonna allow me to react to all of these competitive changes.

And then they say, yeah. But we really don’t have competitive data. We don’t have a way to capture that, and everything’s negotiated. Unless it’s an RFP, you know, I’m probably not even gonna understand what the final price was unless somebody’s, you know, feeding me information saying, hey. You need to come in around this. Like, it it’s way more of a a market signal in your industry than it is, like, true competitive intelligence.

So we’ll we’ll show how the the business and the pricing organization can have control over that and manage that at at different levels to make sure that optimization is at least aware of it. You may you may or may not wanna utilize it, but it it needs to be aware of it to make sure you’re showing the final correct price to a salesperson when they’re out there negotiating with, with your customers.

And then last but not least, this one’s a little bit more complex, but I need to use all my data at the right time. I’ll I’ll hear a lot about that. So, you know, with the power of a pricing engine, I can react immediately to all these opportunities and all these these, change triggers that I have in my industry.

But a certain percentage, sometimes high, is on a contract. It’s not spot. So when I change price, it’s not affecting a good percentage of my business. So how do I make sure that I’m I’m, keeping track of all these changes?

Does it mean I I need more complicated, you know, analysis of my business separately? Like, I don’t have the right resources to do all of this. So we’re gonna talk about how using all of the data to make sure the optimization is is really picking up on what that market definition of price is. Doesn’t mean we’re gonna drive everybody race to the bottom on pricing, but how do we make sure that we’re visible of things that might be changing faster to inform things that might be changing a little bit slower?

So, I thought this was a fun way to kick it off with with a couple of things we typically and it’s almost a push and a pull of sometimes the same person we’re talking to of this this great idea and then they kinda come back down to reality. How are we actually gonna solve for this? And, it it’s actually fun when that happens because we’ve we’ve got a solution for that, and we’ll be, we’ll be walking through that here the the rest of the webinar. I did wanna level set a little bit, on price management versus price optimization.

Sometimes there’s, like, a a real line drawn there when you think about things that I manage versus things I can optimize. So management and and I have greatly simplified your industry with suppliers, distributors, and and end customers or operators.

I know there’s a a lot more uniqueness there, but I wanted to keep it simple for this because, when we talk first about management, what that usually means is, well, I have supplier cost changes, and I might have tariff information or or production or freight or fuel or or anything else that is really it’s almost a pass through. Like, I need to be aware of it. It affects my margin, but I don’t really have that much impact over what it is short of, you know, maybe negotiating better with suppliers.

But when we talk about optimization, what we’re talking about there is that that real opportunity to maximize profitability.

This is where you’re negotiating with customers. This is where you you might have those contracts or it could be Spot, but you have a customer that is asking for a product or a series of products and you know, the quantity that they need, you know maybe what they did last time because we have that historical data, we we understand seasonality, you know, delivery dates, contract dates, ship to dates. All of that information is really valuable in in determining the the price that you should be able to charge to that customer.

So with that said, I’m gonna go a little bit further here and and talk about a problem statement here. I typically don’t read off of slides, but I I am gonna read this one word for word because it’s it’s important.

So this is, for example, a selling situation that that you could be in. Your salesperson saying, hey. I need to deliver a competitive winning price in September for ten specialty SKUs for a spot order to a small size local chain that is an existing customer in Nashville, Tennessee. Pick Nashville because that’s where I live.

So when you look at this, this is, you know, you’re kinda describing a quote or you’re describing somebody needed to to get a price for an order to a customer. So how do I take all of the information that I already know for for the most part in my data and use that to create that winning price? So I’m gonna I’m gonna break these out a little bit so you can see the the richness in the datasets that you probably already have. It’s just using the right algorithms to make sure you’re coming up with that one word, winning price.

So, I’m picking on a couple of these, but winning price, that’s the first one. That’s what we’re optimizing for. They’d come up with the best price that reduces reduces churn if it’s an existing customer and improves my win probability if it’s an existing or a new customer.

I also know the the time of the month or the time of the year, so I can pick up on seasonality. I understand that these products are are behaving differently in in the summer versus in the winter. I know the quantity. I know the SKU type.

Is it specialty? Is it commodity? Is it a configured product? Is it a private label product?

I know everything about that SKU and every attribute, everything that describes that SKU.

I know the the type of order. I said it was a spot. It’s a a small size local chain. I’ve got the customer type.

I have history. I know it’s an existing customer, so I’ve got history. But I don’t have history. I can still use the other data that I have to help me inform my decision making for someone who might be a new customer.

And also geography. So I’m gonna walk through some examples in the application, that show a little bit more about where and when these would be used, but I thought this was a a great way to to start off kind of what what we’re solving for initially.

So AI in general. So, you know, PROS uses AI, obviously, to come up with a price recommendation. We use a a neural network to do that. Those have been around for a long time.

I’m sure some of you guys are are probably familiar with those or at least how they they work to some degree. And like I said, they’ve they’ve been around for a while. So AI is is being adopted in the distribution business across the board, not just for pricing. But if you think about, everybody’s using AI to I mean, it could be crafting an email.

It could be, you know, helping manage your calendar. It could be, you know, like, when I whenever I ever think about, like, distribution, I I’d go to, like, all these crazy robots and stuff running around these warehouses delivering, you know, delivering things where they need to go to to get delivered. You know, that a lot of that uses AI. Planning in your supply chain uses AI.

So the the initial challenge is, you know, why not in pricing? It’s being adopted everywhere else. So why why would you not think about using this in pricing?

So how do we solve for my last problem statement in a way that’s easily consumable by sales? So we’re gonna start by talking about where AI lives, how it works, how you can provide the recommendations and the detail to the pricing team. And then, at the tail end of this, we’ll talk about sales consumption because you have to make sure you get the this price recommendation in the hands of of, of your sales team or whoever is executing on that price recommendation. So how does the science work?

How does it work for distribution industry? How does it solve those three challenges we list listed upfront? How do I prove out that not only it’s a fit for my business, but that it’s given me the right level of detail, but I have confidence in the recommendation, and how do I get it in the hands of the sales team? So we’re gonna be covering all of this.

So I’m gonna dip, dive now right into the the application and show you some screens, some really important screens that I I think are are are really powerful for, analyzing the the recommendation and how we get there. So I’m gonna start by kinda hovering in on this this top section. This is my problem statement.

You know, I don’t have as much detail here, but it’s my seasoned crinkle cut fries, and it’s purchased by by this, particular customer. So when you think about what pricing pricing needs to see, what’s all the detail behind the scenes? And I’m gonna kinda walk you through lower and lower and lower level of detail so that you can understand how an application like this does give you give you the confidence.

So, okay, great. We’ve we’ve established our our problem statement. Now what is the end result? So we’re asking a question and we get the answer.

So the answer is, what should what should the price be that I should charge to this customer?

You know, getting a pricing envelope or a pricing band is is pretty common. Floor, target, expert. There’s there’s lots of terminology out there to provide.

You know, this is the range I should typically stay at to to try and win the business, get the the highest probability to win. So what you’re gonna see is within my floor target expert that these are dynamically generated using using AI, using AI and the PROS platform. These are based off of your customer, your product, and your transactional information to show where you’ve historically won, but also predicting the future. So if you look across the screen here, and now I’m starting to see our target recommendation, it’s twenty four ninety eight. Fantastic. You’ve got a seventy six percent probability of winning at that price.

Not a huge increase. It’s one point eight percent increase, which, you know, if this is a customer that it’s the most important product for them, maybe that is a lot. But if it’s something, you know, where the industry’s going up, their tariffs are getting a twenty percent, I might be exaggerating, but twenty percent increase from our suppliers, they’re probably gonna be feeling that from everybody. So this is this is giving you that, again, confidence to understand that, yeah, I I can probably win at twenty four ninety eight.

I don’t wanna go below twenty three seventy four because that’s really the floor recommendation, where I I might even wanna think about walking away. But this is this is a business decision for you guys. Floor doesn’t have to be a hard floor. It can just be, this is the lowest price I want my sales team to see.

I can go a little bit lower. I could go all the way down to to cost neutral if I want to, if this is a really important product for this customer, but it’s about the visibility.

And then expert, obviously, you know, that’s kind of pushing the top of the envelope. That that could be if I go higher than this, my win probability really drops off. I’m introducing a churn risk for somebody that maybe has been a customer for a long time. So, again, more visibility into the into the recommendations.

So if we move a little bit further down, I love the the statement here. It just says, how did we arrive at this recommendation? It’s it’s showing me now all of the detail. So the the AI that PROS uses plots that same floor target expert price recommendations against the expected win probability at that price. So you can start to see that as I I’m gonna go left to right on on the screen here. One of the, you know, common misconceptions, I would say, in in AI is that it’s, like, we we talked at the beginning, it’s too dynamic for b two b. So it’s am I gonna be able to understand all the detail behind these recommendations?

You know, PROS provides that that detail. So you can see floor target expert. What’s the price recommendation?

What percentage increase or decrease is it if you if you look at the floor? What is my expected win probability? How does it change as I navigate that curve, that that profitability curve? And then show me more information around, you know, revenue per unit, for example.

So these are are great ways to start stair stepping into detail. Now there’s one final step here that is just as important, and that is, how did I actually calculate this? Like, what what values did I look at? We we mentioned customer product and transactional data, but how did I use that to to get to the price recommendation?

So it’s it’s kinda like, I always describe it as, you know, if you’re using a a chat GPT or a Copilot, you can ask it a question. So my question in this case was, what’s the price for this product for this customer? And it gives you a nice elegant answer. It says, twenty four ninety eight.

That’s your target price. So what if I wanna show even more information, like, to my chat GBT or my Copilot example and say, okay. Great. But now what were the factors that drove that?

It’s like, here’s the links that got you to that elegant answer. Here’s the, you know, websites I surveyed to get there if you’re using a, you know, a chat g p t. This is the same thing. It’s interpreting that recommendation.

Twenty four ninety eight, but how do I actually get there?

So if if you think back to the way I started and and using, make sure I’m using the correct data without overcomplicating the solution, this has to be explainable. It has to be explainable really quick. Because if your salespeople are are seeing twenty four point nine eight in, you know, their quoting tool and their portal and Excel, whatever they’re using, to quote, this can be that back to forth conversation with pricing. You know, okay, great.

I’m I’m seeing twenty four ninety eight, but I don’t think it should be that. What what was I using to actually come up with that price? So it’s using all of your your data at the right time. So if I walk through a couple of weeks, because these are all these are all super important.

The customer. Who’s the customer and who am I selling to?

Do I have any detail about this customer because they bought from me before?

It it could be, you know, inventory information. And and just like competitive data, like we mentioned upfront, this can be directional. It doesn’t have to be specific. You might not have a real time way of pulling inventory data, just like you probably don’t have a realistic way to pull competitive data.

You could say that for this, product out of this DC, my inventory is low or my inventory is high. It doesn’t have to be an exact number if you don’t have that level of detail. It’s very flexible to interpret those those data points depending on how specific you you can get them to us. You know, one thing you’ll notice, geography is not on here.

Well, maybe geography is not important because I’m selling this and it’s a it’s a national account. And so it’s not important. I’m buying it at the parent level, so it really doesn’t matter where they’re at. If this was a local spot deal, you would probably see geography on here as a a very important attribute because people that negotiate in Tennessee versus New York versus Wyoming probably are using different products, probably used to paying a different price, and their sensitivity on price change is probably different.

So this is where we wanna make sure we’re not pushing you into a static segmentation.

We’re making sure that your optimization is dynamic. Use the right data at the right time. Don’t force me to use the same five things every time if I need to use seven one time and twelve another time. Like, use the right data for this selling situation.

This is this is really important in a couple of things. One, coming up with the right recommendation. Like, you have to look at the right data. Don’t don’t force myself to use something that is only gonna be relevant half of the time, but I have to use it because it might be relevant some of the time.

That’s a good way to lose confidence in the price recommendation, especially when you start rolling out technology like this. You’re gonna have these change management conversations with with your sales team, with your sales leaders. And if they’re not confident that you’re using the right data to come up with a recommendation, then when they see twenty four ninety eight, they’re not gonna use it because they know that half the time that you know, data we’re using is irrelevant. So I don’t even wanna use it when it’s a contract deal.

I only wanna use it when it’s spot. Like, make sure that all of that is is used at the right time and that it’s truly dynamic.

So when I I talked about dynamic at the beginning, a lot of times, at least when I think about dynamic price, if I’m buying something online or an airline ticket, I I think it’s going up. It’s changing. Like, dynamic is also making sure that the way I’m calculating it is dynamic. I’m dynamically looking at the data.

Everything is kind of purpose fit for this request. This customer is wanting this product at this point in time for this volume on these dates, and here’s all the other things they’re buying. Make sure I’m looking at all of that when I come up with that best optimal price recommendation.

Hey, Daniel. I’m gonna chime in. So I I love that change management sort of conversation because that’s one of the things that I was thinking about, right, as you talked about all the different factors that come into play. So I’m assuming there’s a real consultative approach to each individual distributor, right, to really understand that dynamic, you know, to get it right, like you say, all those different factors, but then to really make sure your Salesforce, you know, believes in that number and feels comfortable.

I’m sure there’s a real, sort of arc there to getting everyone You got it.

Everybody’s in a different place. Yeah. You know, even if you took, you know, do complete direct competitors and you fed them into the same model, they’d get different price recommendations.

Right.

Because they’ve done things differently historically. They might have a different way that they negotiate. I I give power, you know, regional versus, you know, central versus decentralized. All of that is gonna is gonna change that. So, you know, our our implementation approach is obviously to let the science provide the recommendations.

We don’t wanna over engineer the optimization because then we might just be forcing in all of all of behavior from before. But we we do wanna give you the flexibility to to bring that business strategy in, which we’ll talk about a a little bit later.

Yeah. So my other question would be, you know, we talked a lot about pricing and all those factors. Where does margin come in here? Because, obviously, in our world, you know, margin, it plays a key factor. So how do you consider that here?

Yeah. Yeah. Good good question. So, I almost consider price optimization as as being the buzzword.

Like, if you go on LinkedIn, you go on Google, or read books, they’re talking about price optimization. It’s always price. You know, I I don’t know the percentage. I would say more than seventy percent of the time when we’re talking about distribution, it’s margin.

We’re optimizing on margin. Now you turn it into a price real time. It really doesn’t matter for the sales team. If you want the sales team to see a discount off of a list price or or they’re used to seeing a margin and and and a sales facing cost to get the price or or they wanna see a price.

But, PROS optimize on price and on margin and on discount markup. Anything that that that we need to optimize on around those to make sure that we’re fitting into your business strategies is is what we do.

Specifically around margin, I was talking at, it was at at our conference last year, and someone was talking about cost plus. It was a roundtable. One of the one of the questions was, is cost plus a good pricing strategy? Yes or no?

And, you know, everybody I I didn’t even have to chime in. Everybody was talking about how how how bad it was and how old and antiquated it was, and the the conversation started to shift a little bit. And they said, well, maybe cost plus isn’t bad. I mean, we’re here at this pricing conference, and they’re talking about optimization.

Like, cost is what’s really going on. Cost is where I’m buying it from my supplier. Everything back to that three box diagram I had at the at the beginning, that’s that’s where my market’s headed. That’s my signal.

So if the plus is optimized, then cost plus is a fantastic solution because you’re optimizing on that margin. You’re doing the exact same thing that I’ve talked about and shown here, but instead of price, it’s margin. So let me layer on my cost. This is where that optimization and price management get tied together really nicely.

And whenever my cost change, make sure I use that as my reference point to come up with my price recommendation for my sales team. So your great point that it it is it’s very important, and I would say it’s it’s definitely common in this space.

Alright. I’m gonna move on to the next piece here. So, we talked about, you know, I I I decided to title this one eliminating pricing and guesswork or eliminating guesswork and pricing because, like I mentioned at the beginning, this is this is fantastic. I have twenty four ninety eight.

I’ve I’ve got my price. But if I can’t get it in the hands of the person that needs to use it to get the order placed, to take the request from the customer to change a contract price, it’s worthless. It’s it’s gonna it’s gonna get me nothing because I’m gonna have to go through. I’m a have to proactively scan all of this and find it and, you know, millions and millions of price points.

And so what what we really focus on is, you know, getting that into the hands of the the sales team real time. So we utilize state of the art, you know, real time pricing engine to deliver that directly into your sales tools in sub three hundred millisecond response time. That’s that is probably one of the most important topics that we have when we’re we’re going through these demonstrations. It’s always, you know, I need to make sure it’s a fit for my business, how does it work, which is everything we just talked about.

But then it gets into, how am I gonna get it into the hands of the sales team?

So this could be our CPQ. We have customers that are using our CPQ to do this. Some people, you know, maybe they have a a simpler coding process and they’re just delivering it directly into opportunity in Salesforce or in Dynamics.

I put Epicor on here because Epicor is a a a really common ERP that we run into, in distribution in general, not necessarily, you know, only food service, distribution specific, but distribution in general. And, you know, I I need to make sure I’m working with something, you know, that that may be, you know, older than another technology. Those are those are all things that are important. And then the last one, I should have put a label on it.

It’s kind of hard to understand what that is. That’s customer reporting. What, you know, where where are people using, what are they using to consume pricing, negotiate with customers, process orders, take orders, manage contracts? This means get it into the hands of the salespeople regardless of how they’re using it.

This could be Excel as well. I mean, the the what the beauty of it is is what’s on the left hand side of the screen really doesn’t care about the right hand side of the screen. All all it needs to know is something is gonna be asking for a price, and I need to respond real time. So if you’re navigating from one to another, maybe you’ve been quoting on a legacy ERP and you’re you’re moving to a CRM in in two years.

We we hear a lot about that, and they’ll they’ll say, well, we we probably need to wait. This might not be a a good time for us to do something because we’re gonna have to change everything when we move to our new tool. Well, this this, optimization, it almost I always say it thrives in chaos because it it almost doesn’t want structure. You know, if you’re if you’re, you know, changing your ERP or you’re changing your CRM, there’s a lot of structure and decision making that has to go in there.

And optimization, it’s looking at this raw set of data and making a real time recommendation, and then it sends it where it needs to go.

So regardless of where you are in your, I would say, technology journey on the the sales side, on the the consumption side, optimization is fit. It it it’s a great way to to also help with change management with other applications because if you’re getting a winning price recommendation, you might have better adoption for other tools where it’s being consumed.

Yeah. I mean, I I was just gonna just chime in. You know, we we see this with so many different members. Right?

Everyone’s stack is totally different. Right? And there’s this sense of if I’m not at x, y, and z higher level, like, I can’t implement this type of technology. So that’s really interesting to hear.

You know, I think, I think it it’d be interesting to know, and and maybe you have that, but, you know, I’m sure you have some numbers around even through using the optimization, you know, how that helps from a you know, how it helps save money, right, to to manage that stack more effectively.

Yeah. Absolutely. You know, if you if you think about, using let’s say you’re using Excel for pricing and quoting today.

If you’re moving to a CRM in the future, it’s gotta go somewhere. It’s probably not gonna stay in Excel. So you’re faced with the decision of, what am I gonna do with that? Am I gonna build it out directly in my CRM?

CRM is a fantastic storage location for for lots of data.

But if it’s if you’re a distributor and you have hundreds of thousands of of SKUs, maybe even millions of SKUs, you know, tens of thousands to fifty thousand plus customer. Like, the amount of of data that you’re gonna have to store in that application and hope it’s able to perform is is gonna be staggering. So, this we we don’t talk about it as often. This this is a really good point, but the, you know, the the cost savings compared to doing something differently or continuing to do it how you’re doing can be substantial as well because we’re we’re not out here calculating all of these recommendations and storing them every time.

This is a real time request. So I need a price for this. Go get it. I’ve I’ve always heard the example of, you know, using a segmentation.

If you’re selling snowblowers and you’re you’re selling them geographically, you’re going to have a segment full of price recommendations for snowblowers in Florida, and you’re never gonna sell any of them. Maybe you will. I don’t know. Someone someone probably has a snowblower in Florida.

But you get the point there. You’re you’re creating a lot of excess noise that you have to manage somewhere.

So that that’s a that’s another good proof point for using optimization in the right spot and just sending it where it needs to go regardless of of where you’re at in your your tech journey.

Great. Thank you.

Alright. So I’m I’m circling back to my my three problem statements here. So the the first one was on, suppliers are increasing cost. So my challenge would be here, like, be the right level of dynamic for your business.

Like, AI pricing technology should help drive a better result, not fundamentally change your industry because you’re just like, we had that supplier, distributor, and end customer. You’re just a a component of a much larger supply chain. So create the the correct level of dynamic for you and focus on that that incremental increase, in margin. So one of the things we saw in that screenshot had a one point eight percent increase in price to the target price.

That had a that had a decrease in in the floor. Now we were meeting with a distributor, a couple weeks ago and I’ve it wasn’t in food, it was in a a different industry.

And, you know, we we talked about price decreases, and they said, this is this is fantastic. Like, I I don’t I don’t necessarily want a solution that’s gonna say all of my prices should go down, but I want a solution that is gonna tell me when I I might get caught speeding. Like, compared to everybody else like this customer, they are substantially high. You’re you’re winning, but you’re historically, like, they are a customer. There are transactions.

But if you look at just holding them at their current price, you’re getting a a not very good win probability.

So what do you typically see happen? In distribution across the board, I I have all of these these products that these this account is buying from me, and, one of them disappears, and I don’t pick up on it. They’re buying two hundred things from me. One of them disappeared.

The volume’s gone. If you’re not proactively looking for that, what happened? Well, they might be testing out with somebody else. Can this person fulfill my order?

Is it gonna show up on time? Is the quality right? If it’s a frozen product, is it still frozen when it shows up? And then the next month, what happens?

All the volume’s gone. They left. They’re not on contract. It’s a it’s a weekly order, so you show up next week, and they bought it from someone else.

It’s gone. So making sure that, you’re you’re doing it in a smart way and and proactively focusing on on places that might introduce risk in the business.

On my the competition is undercutting me. So let’s let’s talk about that one a little bit. You know, giving giving business and this is really targeted to, like, business and pricing and finance and sales ops. Giving them the flexibility to to create business rules, to to put the strategic levers on top of optimization that these people are gonna know.

There’s gonna be things that, you know, a a financial function, a business team knows that the data and the AI is not going to tell you. If you make a strategic change in your business, the AI might not pick up on it this week. It might pick up on it next week, but you can tell it to do things for a short period of time. I know that in this region, in this geography, for customers like this, I am I’m getting kicked out on the street, because I’m priced too high.

So I wanna take, you know, a ninety day pricing rule, and I wanna reduce all of my prices for crinkle cut fries sold in Tennessee by ten percent. And that’s what the recommendation says. So when sales consumes their price real time, they don’t see twenty four ninety eight. They see it minus ten percent.

And then that rule slowly phases out where you can you can keep it for an extended period of time. But give the the the business the flexibility to manage and do what they’re really good at, understanding your business and your market, but just layered on top of the optimization. That’s something that that we see as being really, really effective for our customers.

And then lastly, using all all the data at the right time. So what I kind of summarize on this one is be situational aware. Let your datasets, talk to each other without creating a a race to the bottom. If you think about the screen that I showed that that had the price recommendations, what this really means is let let’s take, contract and spot, for example. You have certain percentage of your revenue is contract, a certain percentage is spot.

You probably don’t want them to be the same. You you have different buying behavior for contract. Maybe it’s larger customers that are on contract demanding higher higher volumes.

So I you you might think, I don’t wanna look at my my spot business with my contract business or my street business because, it’s gonna it’s gonna change the price to make one too high and one too low. That that’s not what we’re saying. We we want it to be this specific recommendation for the selling situation, but you may pick up on market moves in one part of your business faster than the other part of your business.

So for for example, I’ve I’m, I don’t I don’t necessarily wanna price my contract exactly at my street. But if street is changing all the time when I have orders and the price is is going up constantly, I wanna I wanna make sure I’m looking at that data. Don’t make them have the same price, but I may I may pick up on moves faster. And it could be across product as well.

You know, maybe this could be a terrible example, but, hot dogs and ketchup could be one example. Maybe everybody buys, you know, ketchup all at once and they only buy it three times a month, but they’re buying hot dogs all the time. So if if there’s a shift in in demand for one of for for my hot dogs, it’s gonna have an effect on my price. So I probably want that to be a comparable product.

So I know that, hey, something’s coming with the ketchup. You’re you don’t price it quite as often. But something that is typically bought hand in hand is having a shift. That’s that’s another way we we think about what what does dynamic truly mean and being aware based off of the situation of of that data.

So to kinda wrap this up here, and I I promise we’ll we’ll pause for questions here at the end. I wanted to talk about, you know, three three main things. You know, one of them is the the impact of optimization.

And I was thinking about talking about, you know, market research here. And the first thing that came to my mind, I think it’s a perfect example. So I’ve got two young kids. Carol and I were talking about this, yesterday.

And we’ve been rewatching an old show called Reading Rainbow. I don’t know if anybody’s heard of that or seen it. I’m I’m sure you have. And, the host, LeVar Burton, always says, you don’t have to take my word for it.

So that I should have had that as the theme on here. And you really don’t. You don’t have to take my word for it, Pro’s word for it. This is a real thing that’s going on in your industry.

We talked about AI being used for other functions in distribution, but AI is being used for pricing in food service distribution. If you look at Gartner and IDC and G2 and Forrester, the impacts to your top and bottom line for using price optimization is is substantial. So just the fact that that people are using it is incredible. And then obviously, PROS is a leader in in those those quadrants as well.

You can you can go look that up, do some do some own have your own digging there. I won’t I won’t talk too much about it, but it’s it it really is happening, and it’s it’s real in your industry.

So if if it’s something you’re interested in, then then you gotta figure out, is it right for me right now? Like, it yes. It’s happening in the industry. But, you know, how do I determine if this is the right opportunity for me to address this now?

You know, think about what else is going on in in your technology landscape. We talked about integration and other tools. It might be a great time and you weren’t even thinking about it. You know, you’re you you might be thinking this is a bad time because I’m doing something else on over here.

But if if optimization really is is kind of this self sufficient engine to generate pricing, it’s it’s very easy to plug it in where it needs to go. So really think about all the other initiatives, in the events that are happening in your organization that that this might be really well attached to to make to make sure you’re you’re driving the value you’re expecting.

And then last but not least here is is the timing right for you? This is this is a big one. So obviously, why now as an industry, but is the timing right for you as as a particular distributor?

Stakeholder analysis is huge. You know, making sure that you’re talking to, anyone that would care about pricing. And your typical thought is gonna be, well, it’s my pricing team and it’s, you know, my finance team. But it’s also your sales leaders.

It’s also, you know, sales ops, rev ops, if you have that team. It’s it’s your finance organization, but it’s also your IT organization. Because, yeah, I gotta get the data to PROS somehow, and I need to make sure I can get it out of PROS real time to to show it where it needs to be. All those things are are things you need to start talking about.

So I’m I’m obviously here to evangelize, price optimization and and using AI and food service distribution.

But, you know, the next step is really socializing it on on your end. So like Carolyn mentioned at the beginning, you know, everybody’s gonna get the the reporting here. But we’ve also, you know, got information that that we can send you directly from PROS. You can go to our website. I’ve got a QR code at the at the end that I will show you. It takes us right to our website. But I will hand it back over to to Carolyn and see if we had any questions.

Yeah. So, Daniel, I’ll you you sort of teed it off, you know, the the, you know, idea of, like, socializing it within your organization and and all, you know, gaining that buy in from all those stakeholders.

We’ve all probably been on one of these project teams in the past, and that takes time. So with that, you know, what is sort of that start to finish time look like to, you know, to sort of bring something list this onboard to an organization?

Yeah. Usually, give a range just because everybody’s different. Just like you said, everybody’s data is different. Everybody’s timelines are different.

It’s usually in the three to five month range. You know, where it is closer to the three month is, you know, you maybe you already have the systems that are plugged in that you’re just gonna say, let’s take our old price that we were, you know, calculating ourselves or pulling from somewhere else, and we’re just gonna feed it right into our tool. You know, some organizations say we want a a little bit longer user acceptance testing because we wanna, you know, do some control group rollouts to make sure I’m understanding the value the correct way. So lots of ways to roll it out, but, you know, kind of that three to four, three to five month, time frame’s a a good baseline when you think about how do I get optimization up and running in in my business.

Right. I have another question. So I’m I’m gonna ask sort of the elephant in the room. Right?

With this whole AI conversation across the board. Right? It’s where is the cross section of my data that’s that’s mine, and I don’t right? I don’t want it out there.

I want I you know, I I know I know I’m not, you know, so special that it’s one hundred percent secret, but at the same time, I’m definitely not giving my sales team, you know, putting all of our pricing into chat g p t for the world to get. So, you know, how do we tow this line with, you know, sort of the amazing stuff you just showed us, but really making sure that, you know, we feel comfortable that we’ve put the right elements in there, but we’re not giving away our secret sauce.

Yeah. Good. Great question. And that goes, that that’s kind of across the board. It can be product specific things or customers.

I don’t want my customers getting out there. I don’t want my, you know, I I have unique SKUs that I don’t think anybody else sells or maybe sells the way I do, so specialty SKUs. I don’t want I don’t want that getting out there. So from a security standpoint, you know, we have the the top security certifications and and compliance that you’ll find out there on our our website, if you wanna do some research and digging into that to make sure that the data itself is secure, but then the crossover between AI.

So how do I make sure that this isn’t feeding into a a larger model? Well, it it’s not. This is not an external connected to Google learning from things that you don’t know what’s feeding in and feeding into other LLMs. That’s not happening.

This is completely siloed. This is your data.

No one else’s data is gonna influence your prices, and your prices aren’t gonna influence anyone else’s data. You know, that that fundamentally isn’t something that that we’re about. That’s that’s not how you should be doing business. But but also it doesn’t make sense from a business standpoint anyway because someone selling at this price and you selling at this price, they their, you know, basket of things that they’re selling, their quote might look completely different. Like, it’s not apples to apples.

So what again, to kinda summarize, it’s not something that that PROS does. Everything is is isolated and siloed, but, you know, from a business standpoint, probably not a good practice anyway.

Right. Right. I guess my last question would be, there’s a lot of elements here, probably a lot of you’ve indicated a lot of learning along the journey. Right?

So how how active does PROS really stay involved, right, beyond that you know, beyond initial implementation. Right? But how do we this seems like it’s something that’s gonna be so generative over the life of my business or you know? I’m assuming this is something we wanna consider having a really strong relationship with.

Yeah. I mean, you’re you’re partnering with someone for for the long term. I mean, we’re we’re a SaaS company. We we want you to be a customer and and renew and innovate with us and upgrade to new things that we have.

That’s, you know, that’s the way that we think about our customers. We have a lot of, excuse me, a lot of long term customers at PROS. And we do that a couple different ways. Every account has an executive sponsor.

That’s that’s key. When I talk about stakeholder analysis upfront, that’s part of it. Making sure that, you know, before you’ve even inked a deal with PROS, hopefully, that relationship is already connected so that we have, you know, the driving the same behavior on on both teams. And we also have, customer success organization.

Every account has a customer success manager, and they’re there to help you if you wanna navigate something simple or something complex. It could be as as simple as, hey. What does this button do? It could be as complex as, hey.

I’ve I’ve got a completely new dataset. I want you guys to help me think about how I could use it or maybe if I should or shouldn’t use it. We’re we’re always there to help. That’s for sure.

But the, our customers are are very self sufficient. So I kinda tow the line on, should you even talk about this or not? But, like, the solution’s very configurable. If you wanna add a new business rule, if you wanna add a new, you know, column somewhere, if you wanna look at a new dataset, a lot of our customers do that on their own.

That doesn’t mean that we’re not there to help, and we want you to just go on and figure it off on on your own. We’re we’re happy to help in whatever level you want us to help. But, you know, it like you mentioned, for for optimization, it’s a it’s a very self sufficient application.

Yep. Yep. Well, super. Well, I I’m sure maybe you can go ahead and put up the QR code for anyone who is interested in learning more.

And, I appreciate your time today, you know, diving into this. This is this is a topic we’re hearing a lot about, you know, with our members. We’re all you know, two years ago when when I joined IFDA, this AI, you know, was not something we talk about now. Now, you know, it seems like it’s predominant in in so many aspects of what we do.

So, you know, appreciate really the time for you to dive into this with our members.

Awesome. Yeah. Thanks again for the time.

Yeah. Well, we appreciate everyone joining us today. Thank you, PROS, again for being an Allied member and supporting, IFDA as well as our distributor members.

Again, probably about twenty four to forty eight hours, we’ll have this up on our website. So, feel free to share with anyone who you fee think, will enjoy.

Awesome. Thanks, Daniel. Really appreciate your time today.

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