Navigating the Future of Pricing Strategies

Margaret (00:08)

Welcome to the Kodaris Community Show with your hosts, Tony and Margaret and the occasional friends stopping by. This is the podcast where we explore how innovation and technology is reshaping distribution and the supply chain as a whole. Discover how technology is making companies more efficient and profitable, making their customers happier, and is paving the way for our future. Join us for insights from industry experts, interviews with innovators, and actionable ideas to stay ahead in our rapidly evolving world.

Margaret (00:42)

On this episode, our very own Executive Vice President of Platform Solutions, Mike Mazzoni, joins us for a conversation about pricing. Mike joined Kodaris in August after more than 12 years at Infor. He has a master's degree in industrial technology from Purdue University and 25 years of experience driving continuous innovation to the wholesale distribution market space. Here's our conversation.

Margaret (01:07)

Welcome Mike and Tony. So, today, we have a really interesting topic: we are talking about pricing. I’ve heard specifically, Mike, that you have a lot to say about this. So we're very excited to have you join us today.

But Tony, I want to start out with a question for you of how did Kodaris even get involved with pricing at the beginning? What kind of bubbled up that got your brain interested in it?

Tony Zakula (01:40)

Well, in the beginning, my brain was never interested, because only geeks think about pricing and “how cool is mathematics.”

[laughs]

So let's just be clear. No, so we started years ago in the commerce arena with distribution. And, you know, I think there was 10 years ago, sub-second page loads with pricing was unheard of, even unheard of for some commerce systems on ERPS today. So, how do you get there?

We basically built our pricing engine and emulation to deliver commerce page loads. And then through our experience in that, we actually learned about price engines across different ERPs. We emulate several. We created mods on our own.

Then we built a contract quoting system with some pretty interesting details, you know, changing out pricing matrixes in real time levels in real time, dynamically. It did become fun. It's almost kind of geeky to think about how much math you can interchange and process on all these different factors. Costs, market size, levels.

It becomes a really interesting very mathematical driven problem that you can solve with engineering at scale. Not unlike, I mean, AI is all math, so not unlike AI and some of the things that is being done to the point now, where now there's so many variables, but how do you... What's cool today is not only trying to couple some of that with segmenting AI and all the different things we're doing in real time, but also how do you do that at massive scale? Which is a challenge to our engineering team. How fast and how big can we go? Because to deliver real time information, we can dive into a few of those projects that are pretty cool, but our engineering teams are having kind of a fun exercise on some of the massive scale that we're working at right now with customers.

Margaret (04:10)

Who are the geeks now?

Tony Zakula (04:12)

Well, that's still Mike. I'm the engineer.

Mike Mazzoni (04:17)

You know, Margaret, as Tony was talking, I was thinking back to kind of when I started my career in technology, working with distributors, and it was actually on the eve of Y2K. So some people might know what that means. It was a while ago.

But I went out and I got an opportunity to sit and work out at a distributor for a few weeks, kind of going from department to department, and observing and learning what they do and how they use the technology, how they were using the software at the time. And I actually got to spend two days with, and they said, here, this is so and so, he's in charge of pricing. And that was his full-time job. He just looked at pricing all day, every day. Are their margins good? Are they slipping? Did they have a cost increase? Are they capturing the rebates effectively?

And I guess I never realized having only been in a retail world where you say, well, this costs five bucks and you're done. Right. When you think about what distributors deal with, that same product could be sold for hundreds of different prices to the different customers or segments that they deal with. That could be geographical. It could be based on, any number of factors.

So, the complexity that Tony's talking about is there and it only continues to increase. And a lot of times when we think about, are we setting the price correctly as a distributor? Minute changes and how that price is set can have a fairly dramatic impact on the profitability, not only of that product, but the whole organization.

And that's not, I think that's pretty well known amongst the industry. There's plenty of companies out there that are trying to help with pricing optimization and things like that. And the challenge, and I think what I've been impressed with that Tony and his team have built up, is the tools to really embrace the massive amounts of data that's out there with these different pricing methodologies and pricing engines.

And not only calculate the price, but do it in a very performant way. So, anyway, just a little history of when I first got introduced to pricing, and what the complexities are and a lot of the challenges and the complexity, they haven't changed. The challenges are still there and now that the technology is better. So how can we take advantage of the technology to really address the massive amount of data that needs to be looked at?

Tony Zakula (07:03)

Yeah, I think it's when we talk about scale and pricing, we did a ROI study on a feature like, I think it was proof of delivery or order automation or something we did years ago. Some of that, we knew, we calculated if we could reduce just the delivery error rate half a percent for this one particular customer, they would save something like $5 million a year.

Right, because they knew their cost to send the truck out, they knew their cost to restock, they knew all of those costs. So then if you could reduce it 1% or 2%, but when you think about pricing and what you're selling, if you're doing a ton of transactions and just two of those products on half those transactions are, you could have got one or two more percent margin.

We're talking a massive difference. Or your costs changed, and you didn't realize and your price didn't change. And so now you're losing two points. It adds up really fast.

So it is a mission critical piece of pricing, but with a massive amount of data and a massive amount of inputs coming from different areas, some of the complexity can be even, we're delivering to this customer 10 miles and this other customer 40 miles. Should they be paying the same price?

Should there be an add-on charge for certain things? Is there a better way to get it there? Maybe drop shipping, shipping direct. This continuous analysis makes it kind of a cool space to think in and engineer in and try to be improving that.

Margaret (08:58)

And I imagine that speed of having access to the right data is important to make an accurate decision on pricing.

Mike Mazzoni (09:10)

For sure. And I think this has always been the case, but maybe there's a magnifying glass on it these days with the fluctuation and costs that distributors are incurring.

Well, as those costs change, number one, it becomes critical to be able to identify and consume those cost changes, get them into your system so that we can do, so we can start to see what the impact is gonna be to the price and to the profitability or the margin. And so again, massive amounts of data that could be–we know what distributors deal with, they deal with tens of thousands or even hundreds of thousands of SKUs across potentially dozens or hundreds of locations.

To be able to take those cost changes as they occur, to ingest them and then actually have them impact pricing or be able to identify what the impact of pricing is gonna be is hugely critical to making sure–and, talk about speed–to making sure that, even a couple of days of not being able to understand what that impact was can have an effect on profitability.

Tony Zakula (10:23)

Yeah, so let's talk a little bit about the, I guess the challenges, some of the inherent challenges we're seeing, some of the ways that we're potentially solving them. I like to dream the future. So when we're talking about pricing in the future, you know, I think one of the things that we've struggled with, not, or our customers have struggled with, the industry struggles with, is you also have to tell your customers what their price is going to be, right?

So you can't...a lot of times there's contracts involved, or there's expectations on customer service. So if you're going to increase your customers' prices, it's not like you walk out to the retail shelf and change the price to the next guy that walks in and pays more, right? There's this whole relationship.

Even if your costs change, distributors will, many times, eat some of that cost for a while to not upset their customers' business. So one of the challenges we face is just, how do you get a price book to a customer who wants a spreadsheet, wants a paper copy, wants something, because they're using it to go out and price to their customers.

That's a big challenge. It's a big challenge we've been working on with our customers, actually solving for a few years. So one of the cool breakthroughs we had recently was we figured out, because like Mike said, the technology is improving, how to stream thousands of product records directly out of the database and price them in real time as they're streaming out into a file, into another database table, to something like 20,000 records a second on a standard CPU engine application server.

Which means you potentially could distribute a hundred thousand price lists overnight. And many customers are, that's unthinkable. Or even getting that, putting that together, emailing it out, doing all of those things. And we worked on that for a couple years. Like how do you make that happen?

And our engineering team took it, did a lot of work, but it's pretty exciting. But let's, I guess, dig into that. If distributing price lists to your customers or making a point and click for them to self-service, I think is a pretty innovative thing. I don't know that anybody else is doing that out there, or they could possibly be doing it, but integrated with your operations where it's automated.

This is critical.

Mike Mazzoni (13:06)

Yeah. I think from my experience, not, historically, these solutions have not been able to do that at scale. So there was always a trade-off. I'm going to do this for my top X customers, or I'm going to do it for only the products they've bought in the last X months. So I'm going to have to start to limit that subset of data because, the ability to process that number of…f you think about, if we go back and think about pricing and Tony, you've referenced the price matrix, price levels. We think about that price hierarchy. The challenge with, this is not a simple, let me go grab the list price field and pull it into a spreadsheet. There is a complex series or hierarchy of calculations that need to occur. Combined with some business logic that says, is it a contract? Is it a promo?

Find the best price for each and every combination of customer and product, or often customer location and product. And so, historically, when ERP companies or when software providers or when customers wanted to run these, they had to make sacrifices and trade-offs. I'm going to limit the subset of data because that's the only way I can get this done. And I still, it's going to take me longer than I wanted to or I needed to.

Certainly every night is not, was never a reality, right? You'd say, how do I limit that subset and how do I determine the cadence which, you know, where I can get the updated pricing to my customers or my internal team? So this is really cool. I think what the team at Kodaris has been able to pull together.

Tony Zakula (14:52)

Yeah, and so then as with AI with everything that's changing technology so fast, there's always the next. What can we do next with it, right? And I think that. So we've kind of got that running. You know, there's always polish to it, but it's really interesting. What else does that enable?

We started out with one of our large customers who says ‘Well, I need to raise prices in two months and then four months.’ And when we did, when we did quoting, we created a spreadsheet view to by product group, by price types, by product, you could escalate. So you could put in dates and ranges and say, well, in three months, we're going to go up 5% and three more months, another 5% so that they could very quickly manipulate some price escalations into contracts. Which was okay on the single quote-type scenario.

But, so now we have this super high speed price exports, then we have that in the quoting and then we said, well, what if you could do that for price increases to your customers, not just singly, but across the system, right? Because you have product groups, segments that are going to change in two months.

What if you could give your customer that ahead of time? And let them know and give them an updated price book? So one of the things we're working on that's, I think, really cool, because now I'm turning to a pricing geek, is could you run price engines and data in the future? Like based on the future where you haven't set up pricing, you haven't changed pricing, but you manipulate groups or products at a high level?

And then you high speed export that to, if they're locked into customer price books. But that led to another one of our ARIS members saying, well, could I put that analysis engine so I could run it three times and stagger it and then throw it into my BI tool or your BI tool and look at it? And when you're talking millions of products, it's a pretty daunting, big data challenge, but it's cool because now we're talking about so much performance. And so many things that you're starting to give the distributor now some futuristic knowledge so they can start analyzing it right. Then I'll stop there, but let you guys comment.

But then I'm going to go back into the other way of, some of the other cool stuff before you even get to that point. Before I geek out too much on it.

Margaret (17:48)

That was going to be my question. So it helps them have a more accurate future forecast.

Mike Mazzoni (17:55)

Well, I mean, it helps them to understand…well, it helps them to be proactive, as Tony mentioned, in communicating with their customers what the potential impact is going to be. To cost increase, or a potential price increase, which, you know, often are driven by cost increases. There's a number of different reasons. And by the way, that's in many cases, that's an opportunity for the distributor.

So the vendor says, okay, or in February, starting April 1st, your cost is going to increase. So they'll take advantage of that to push sales, immediate sales with the customer by saying, hey, we're going to have a cost increase. We want you guys to be able to take advantage of the cost as it is. Let's start to plan.

So there are opportunities when you know about these changes to better and more effectively communicate with your customers in advance.

The other thing is, yes, millions of records, but in the pricing world, a lot of times there are kind of key indicators that you're looking for when you're analyzing current pricing versus future pricing based either on cost changes or escalations. There's a term that we like to use when we talk to distributors about pricing and that's let's look for the low hanging fruit.

So, you know, while there might be millions of records with prices that are being impacted, if we can start to understand customer behavior, if we can start to understand how products perform, and what the vendor's doing with those products. That's all buried in that data. We can start to kind of pick out those opportunities for margin optimization. And then conversely, those opportunities, maybe we need to be more competitive or we don't pass that increase along.

Yes, it's all about being able to get the data and not just what the new cost is going to be, but what the impact of the calculated pricing is going to be. And then we can start to work and figure out how we want to use that across the organization, whether it's a sales incentive, profitability analysis, margin optimization, lots of different ways.

Margaret (20:10)

So you can start to almost predict price sensitivity by customers of if this is actually going to have an impact.

Mike Mazzoni (20:17)

Absolutely should be, and I know most distributors do consider that. The challenge they have is the volume of data and where do you focus.

Tony Zakula (20:26)

Before we back into more of the math, think about what Mike mentioned there. I'm going to have a conversation with my distributors, right? Think about the digital world we're going into. Like doing that in the ERP is one thing.

But if you could show your customer on the website in real time, as they're looking at products and say, on this product, there's a cost increase going to happen on June 3rd. Your new price on June 3rd may be X. So you could buy more now, or click to talk to your sales rep about this price increase. Getting a price book is one thing, but driving, now a real-time future price that your customer can see before it happens, without even having a conversation with them, and without just sending them an email with a spreadsheet.

I don't think that's out there in the industry anywhere, but those are the things that we're thinking that we could bring to market that I think as we, as the younger generations and the whole world becomes more digital, becomes really interesting.

And then going to be an expectation is: I don't necessarily want to have a conversation, but I do want to know the data and I want to know it as soon as possible. And I don't want to have to check my email or download a file on my end to do that.

Or maybe in the Kodaris customer portal, as a customer, I'm putting in my price because I'm a contractor quoting somebody. I want that price increase to happen and I still want to make my 20% markup and I just want the price to auto adjust so I don't have to do anything. Right.

And I, but I want to know ahead of time that it's coming because I might be putting bids out that are 20 days out and maybe I plug that bid in and then auto adjust for that price increase. Think of the margin save not only for distributors, but for customers as you get more integrated to your customer systems and they're relying on you and the amount of work that you've eliminated for your customers.

And maybe well, they'll do more business with you because they, literally, you're taking work off their plate and you're making it easier to do business. So as we talk about the distributor, it also becomes super interesting how you can impact your customer to help them versus, hey, it's all about us. We want to eat more margin. Because you might get a little more margin if you're taking work off your customer's plate as well.

Mike Mazzoni (22:50)

I kind of love that idea of being able to publish a future price or expose the customers, the distributor's customers to the future pricing. I mean, I think distributors are constantly looking for ways to add value to the relationships they have with their customers, really their partners. And information is hugely valuable, because what that can do, first of all, the distributor can guide them to making better decisions.

But their customers can make better decisions in terms of their buying patterns and their overall spend or controlling their costs. So having spent my career in the ERP side and really focused on the business of the distributor, you look at two variables. Can you improve profitability? Can you minimize costs? Well, if the distributor can help their customers to do the same thing, that's added value that's going to bring that customer back again and again.

Margaret (24:17)

Yeah, I think about, I know it's not necessarily a consumer sale, but I think the psychology of having that future price on there, it's like kind of a reverse discount sale, right? Of like, man, I’ve gotta…there's some urgency there, right?

Tony Zakula (24:31)

Yeah, so if we look at just that capability, that's interesting. But then what gets more interesting, we talked about cost increases, which a lot of our customers are struggling with…a cost increase because of tariffs or something like that. And then it becomes the, as you know, we work with suppliers and vendors. As you get that vendor file, could you automate that in?

Like we're working on some of that, automating vendor price files. Then could that cost increase trigger a recalculation across our new pricing, across your pricing infrastructure, your analysis, automatically give you a new forecast, a new whatever? Let you accept that, ‘Hey the cost is going to go up,’ make a decision on price increases.

Or maybe the system, whatever rules that you use in your mind to make that decision. Could that be 100% automated and then that cost update in the ERP and that new pricing implemented? I mean, if you think about it, the amount of analysis that the machine can do with today's technology.

And while you could throw AI in there for grouping and segmenting, which we can talk about in a minute. Some of that, I think the technology is just getting good enough and high-speed enough that you could automate away 90% of that work and let the human make a good business decision.

Versus we import, we get that file by email, we rejigger it, we check an upload, we look at it, then we go to our customers levels, we try to figure out what leveled on, where do we change pricing. You know, I think it becomes super efficient and all that takes a little bit of work, some technology building together.

It's not there today per se, but it's within reach, I think. And then you think what we just talked about across the chain of, you make that decision, click the button, your customers are notified it's showing up. Think about the real time data you can automate from supplier all the way through.

Just your system, the ERP, your customer system. And then the user behavior, did the customer, how did they react to that? Did they just accept it? Did they reach out to their sales rep? Did they, do we need to have a conversation? Did they say, that doesn't really matter. I don't have time for that because it's not affecting my main products. Anyway, it's interesting. I think that’s just one component. We can talk about more components, but. From the supplier side, thoughts on that, I guess, or?

Mike Mazzoni (27:40)

It's huge, to be able to…so in my mind, one of the one of the most important things…so you guys might be aware. But in most business systems that distributors are using, there's a calculated price that comes up. And then the salesperson can click in there and change that price.

And a lot of companies are good at monitoring that. Some have approval processes in place where there's a tolerance that's set. You know, they can't go below a certain margin or discount. But in many cases, that's happening too often. And that obviously is margin leak. So when we talk about bringing in accurate costing and then being able to present that, even internally, to the distributor’s employees and say, here's what we're expecting.

Here's what's coming either today or in a month. Here's what it's going to do to the profitability if the pricing remains the same. Here's what it's going to do to the pricing if we update based on current, or based on some new algorithm. What you're doing is, you're gaining buy-in from your internal employees and a better understanding as to why the price levels are set where they are.

So again, this is something that companies have tried to do with varying levels of success over years, but again, because of the volume of data and now kind of the increasingly frequent nature of these changes, it becomes more challenging. So if we can streamline that process, especially with automated feeds from vendor on cost updates, with looking at calculations on future pricing like we talked about.

What you find is a decrease in those price overrides, because there's an increase in the confidence that we're not trying to take advantage of the customer. The pricing is set at a certain level because of these reasons. And again, the goal being maintaining customer satisfaction, but optimizing profitability.

Tony Zakula (29:54)

Yeah, I think just to add to that or add to the next step, you mentioned price levels. But there typically is just some analysis on where one, where that customer should be based on their volume, based on other things, but two, what maybe where we're winning or losing purchasing.

And so as part of that, as part of the science behind it, can you, with the AI tooling we have, can–and obviously I'm thinking in terms of Kodaris’s system, but can you segment customers automatically based on purchase volume, velocity, all of those things, which there are tools to do that even inside the Infor ERP, but can you get smarter about that?

And then can you have buying programs or targets that are in Kodaris, which we're close on, where if you hit this target on a product group, you're going to get a rebate. But when you look at user behavior, buying habits, volume that they're using, volume going down, volume going up, it's good for sales reps to go out and have conversations. But as price increases come in, if per customer, per segment, a product's now dropped to a C product from an A product for them, do you add another 2% on their…because they're still using it, but not at the velocity they were using it. Maybe they're buying two different products now.

It's almost endless the amount you could start to put into that. One of the challenges with AI is it's so generalized. And you have to have a data scientist or data analyst constantly doing this.

But I think, what we're striving for, with some of this AI and functionality is…AI is cool and it's definitely a tool that we're leveraging. But then there's, what does the human brain in your business actually do? What's the algorithms and can you plug those algorithms on top of some of that AI generated data that you're doing?

But then real time do it, not pull it out and once a month and do analysis. How can that happen in near-real time so that as your business is running, maybe you locked your prices in for 30 days with the customer, but now you got a cost increase on day three. Can you tell the customer on day four, like in 30 days, your price may go up based on a new tariff that just went on and you have 26 more days. Like, I know we're talking future, but again, going back to the beginning, I think some of that stuff's within reach.

It's just a matter of working together with a group of customers and algorithms and things to get there. And that's what's exciting to me is there's so many possibilities to help drive business, not only for our customers, but for them to better serve a distributor's customers. Because that impacts–we know distributors put on the shelf everything we use–so it impacts all of us.

And hey, if I knew...tuna fish was going up and I like tuna fish, I'm gonna stock up, more in my pantry, right?

Mike Mazzoni (33:30)

Sure.

Margaret (33:33)

That's why people invest in those forever stamps, right?

Mike Mazzoni (33:37)

That's right.

Margaret (33:39)

Mike, talk about the challenges, some of the additional challenges of segmentation.

Mike Mazzoni (33:44)

Well, I think, again, if we think about historically and doing this a little bit, we'd start to think about grouping customers together for pricing purposes. And Tony alluded to price levels. So you'd have, we'd go in, we'd say, what are your groups? Well, we have small, medium and large contractors. We have small and medium OEMs, and we have industrial customers. So, they'd have six or eight or 10 different customer types.

And then they'd start to assign groups of products to those groups of customers. And it works well and it worked well, but I think as we start to understand the amount of data, the technology that's now available, now we can start to say: well, this particular customer for a particular product line A behaves a certain way, but for product line B, they're a completely different type of customer. So I really can't consider them a medium contractor when it comes to this.

I'm being generic, but when it comes to these two separate types of products, or even really truly down to the individual product level, that's sometimes where that gold is buried. So this comes back again to being able to understand the data, which includes things like historical sales at the line level detail, it includes how do we look at contracts differently than jobs, differently than rebated products?

We have to start to consider all of these things, but where that data might have been there, or where those transactions were there before bringing that data together, and really understanding how to interpret it. And then getting back to pricing, how does that apply to how we segment these customers for the different products or groups of products, and really fine tune how we want to price those?

This is, you know, there's different terminology out there: pricing, science optimization, profitability analysis. At the end of the day, it's understanding customer behavior, understanding where they're sensitive to certain products and not sensitive to others, and then optimizing that price.

And then really, you know, where distributors may be dealt in tens of thousands or hundreds of thousands of pricing records and price levels on these matrices. You know, now all of a sudden, based on what I just described, we're now into the millions or tens of millions. If I take the number of customers and multiply it by the number of products and the number of locations, you are easily in the tens or hundreds of millions of different calculations or records.

How is that, how is it going to be possible that we can maintain that? And more specifically, we've got this great external system in Kodaris that can process at high speed, can help me understand how the data is impacting not only current, but future pricing. That's great. How do I get that back into my business system so that that pricing hierarchy is in place when my customers are placing orders with me?

And that's another area that we're focused on, working with customers, active projects today to be able to push data back into their ERP, into the price matrices or the pricing records. And we'll continue to look at what the opportunities are there. To not only make better decisions with large data sets, but then once those decisions are made, push that back in and update their business systems.

Tony Zakula (37:25)

Yeah, I think that's a good point. I kind of left that out, we, you know, depending on your ERP, we do write back pricing records near real time. We write back costs. There's a lot we can do as far as making sure that ERP has the data it needs to do the transactions properly.

So it's not just about that. It's also about updating that data when it's needed into the ERP, which, for many years, we've done a lot of work with certain ERPs on that. So absolutely critical.

The other point, though, I thought was that I glossed over, totally forgot about, is distributors do have a challenge in the fact that they may be carrying eight or nine product lines. And those product lines are entirely different. So it's like they are running nine different businesses, even though they're bringing product into their warehouses and shipping it.

When it talks about pricing and costs and everything, those are entirely different businesses. And then you multiply that times a small, medium, large business per product line. And then they may cross product lines, like if you're selling tools, but you have three industrial areas. Now your one line of tools is segmented between three different main product lines and which tools apply to those product lines.

And what's the people who are in those segments willing to pay for specific tools versus a DIYer or who may just need it once. And then you combine that with going to down market consumer or DIY where they're not a business relationship. It's complex and making good decisions.

Mike Mazzoni (39:13)

Margaret, I'm gonna need you to whiteboard all that after this.

Margaret (39:18)

You know what, thank God we're recording and transcribing it because I'll be able to dive in afterwards and really put it up in something that makes sense.

Mike Mazzoni (39:22)

There you go. Complex is the right word.

Tony Zakula (39:31)

Yeah, so wrapping up, obviously we love this. We're geeks around it. I have to, all this complexity and abstraction and everything we're talking about. Honestly, 10, 11 years ago, I didn't know anything about B2B pricing.

It's our customers, our groups of customers working on projects. We are working on several projects with large customers simultaneously, even coming together. Multiple customers are working with us on the same project. You know, without our customers’ inputs and their business challenges, we wouldn't really understand the depth of problems.

So I guess I just want to make that clear, that we don't feel we're solving the world's pricing problems in a vacuum because we know all these issues. We’re solving them by iterating and working with customers and their challenges.

But it does make for a fun technology challenge that hopefully we deliver that back to them. And one of the cool things about Kodaris is–you know, for those who may be new to the conversation–is all that comes back to every customer for free. So, as we work on it with multiple customers and multiple challenges, all those benefits of the problems we solve, we roll out to everyone, is really cool to me.

And we've talked about that in past episodes. But that means if you're a small business, a small distributor, you get the same performance, the same everything as the large guys. But that's what our community is about, is lifting the whole industry.

Wrapping it up, I just say, we're excited Mike joined us. He's got years of experience. Hopefully this conversation was productive. I think it's a peek into what we're working on. People have ideas, they want to join a future discussion around pricing.

We'd love to have our team members on, our customers, our…even if you're not a customer, you’re a distributor, we love to talk shop like this. Thanks everyone for joining and listening today.

Margaret (41:48)

I think we'll have Mike back.

Tony Zakula (41:50)

Maybe.

Margaret (41:52)

We'll keep- we'll keep him around for a little while.

Mike Mazzoni (41:55)

I enjoyed it. Thank you guys.

Margaret (41:58)

And thank you, I learned something today. So I guess in 10 years, maybe I'll be able to wrap my head around it too.

Tony Zakula (42:05)

Maybe.

Margaret (42:07)

Thank you for joining us this week on the Kodaris Community Show.

We'll see you back next time.