The Pricing Leader’s Advantage: Harnessing AI for Growth and Precision

As a pricing leader, you’re at the forefront of your company’s growth—navigating market complexity, aligning internal teams, and delivering results. But with today’s economic volatility and mounting pressure for precision, your role requires more than intuition. It demands technology that works as intelligently as you do.

In this webinar, you’ll discover how AI can become your most powerful ally. Learn how tools like PROS Smart Price Optimization and Management (POM) are helping pricing professionals reach new levels of performance—achieving 90%+ pricing accuracy, boosting operational efficiency by 60%, and delivering up to 20% revenue growth and 5% margin improvement.

Join us for a practical conversation designed to show how AI doesn’t replace pricing leaders—it elevates them. You’ll walk away understanding how to lead with clarity, scale your impact, and future-proof your pricing strategy

Speakers

Full Transcript

Good afternoon, everyone, and welcome to another PPS webinar. We’re so glad you could join us for today’s session, the pricing leaders advantage, harnessing AI for growth and precision. As pricing leaders, you’re navigating one of the most navigate navigating one of the most challenging market environments we’ve seen, balancing complexity, volatility, and the demand for precision. Today’s conversation is all about how AI can become your edge, helping you lead with clarity, scale your impact, and drive stronger business results.
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We’re joined by three fantastic speakers, Matthew Negron, product manager at PROS, Ben Harris, senior product manager at PROS, and Frank Moore, pricing manager at Wilbur Ellis. Throughout the session, please feel free to submit your questions in the chat. We’ll do our best to address them during the q and a portion at the end. With that, let’s get started.

And, Matthew, I’ll hand it over to you.

Great. You can hear me?

We can hear you.

Great. Awesome. Yeah. So here’s a look at our agenda today.

We’re gonna start with outlining some of the key challenges pricing teams are facing in today’s market, and I’m going to explore using Frank’s from Wilbur Ellis’ story, about how they’re transforming their pricing strategies using AI. And through that, we’re gonna introduce SPAM. So that’s smart price optimization and smart price management. We’ll explain what those are and how they can work together to drive better pricing outcomes. And the very end, we’ll wrap up with a nice q and a session, so we’ll give the audience a chance to ask any questions and discuss how these strategies might apply to your organization.

So here at PROS, we’ve had the privilege of partnering with Wilbur Ellis for over six years, helping them helping support their pricing journey. So, Frank Moore, thanks for joining us. You’ve been a key part of that partnership, and you’ve seen firsthand how AI price optimization has driven real business impact. So, Frank, I’ll pass it over to you for a second to share a little bit more about Wilbur Ellis.

Yeah. So we’re just a little over a hundred year old company. Our purpose really is to provide this the essentials, for the world to thrive. So we work alongside growers to generate better solutions in key areas, including water management, resistance management, soil health, organic sustainable, sustainability as and also driving profitability at the farm gate. So, our tagline is really no matter your crop challenge, we have the products and expertise to help you overcome it. But, again, we’ve been a partner of Prozzle over six years now, really helping us to transform the way we price.

Awesome. Thanks.

And before we dive into the Pro solution, I’m I’m gonna ground all of us in the common challenges pricing teams are facing and still facing today. And these issues are coming up up across nearly every industry that PROS works with. So the first one, manual pricing processes are still widespread. They’re slow, inefficient, and prone to error, and this is especially true for when teams are relying on spreadsheets, disconnected tools, or or tribal knowledge. Second, outdated or incomplete pricing coverages causes real financial impact. So it’s whether it’s missed revenue or margin leak leakage. When pricing logic can’t keep pace with the business, you start leaving money on the table.

And third, companies struggle to adapt pricing quickly to changing market conditions. So whether it’s volatile cost, competitive shifts, or macroeconomic changes, lagging price agility leads to lost opportunities.

And finally, complex product portfolios make it hard to maintain consistent competitive pricing. The more products, customers, and rules you have, the harder it becomes to scale pricing decisions without sacrificing precision. So these are the core challenges Pro sets out to solve, and what we’ll show you today is how companies like Wilbur Ellis are tackling them head on using PROS.

So pass it to Frank to kinda share a little bit about Wilber Ellis’ problems.

Yeah. So, you know, as Matt Matthew outlined in the in the preceding slide, when when I came into Wilber Ellis, we were still very manually, cost plus focused.

Really wanted to move to you know, we weren’t able to, quickly change our pricing strategies as the market continued to shift. We also had a very locally focused price effort, so it was not the we didn’t have that centralized oversight to truly understand where we had margin opportunities or where we had, dollars just walking out the door, with that leakage. So we didn’t have a good way to scale it.

So that’s when we decided to partner with PROS to really try to overcome some of those big hurdles.

So, yeah, to address those problems that we just talked about, PROS unifies two key concepts, smart price optimization and smart price management. So this is the, SmartPalm platform that PROS offers. At its core, it starts with data, and and it’s not just one type of data. We bring in all your historical data, customer, and product attributes, and other relevant signals into smart price optimization or SPO as we call it, and that data flows into our AI algorithms.

This is where it’s it’s it’s used to analyze and uncover patterns, behaviors, and price sensitivities.

And we transition that raw data to science backed pricing.

And and what you get is a pricing envelope that includes floor target and expert price, expert price, customized down to the SKU and customer level. From there, the results are handed off to smart price management or SPM as we call it, where your team collaborates with it, setting limits, defining business rules, and adjusting for real world constraints.

We call this the human in the loop moment. So it’s not just automation for the sake of speed. It’s intelligent pricing with governance.

That collaboration layer feeds into your final go to market price, a price that’s aligned to both science and strategy. And whether you’re delivering that price dynamically in real time or through a batch update, it’s always transparent, explainable, and tailored to your customer. So here’s a look of almost the equation where data starts, from your systems, feeds into our AI, where it’s used to analyze the data, and then provides that, pricing envelope. So this is really how PROS is transforming pricing. So from manual and reactive to proactive, dynamic, and strategic.

Starting with smart price optimization.

This is PROS advanced price optimization engine, and it’s what enables businesses to determine the best possible price for every unique sales transaction.

It’s powered by AI and designed to deliver pricing that’s not just competitive but contextual, tailored to the account, the product, and the situation.

SBO’s origins are rooted in the travel industry. So you can think of airlines that are dynamically adjusting prices in real time based on customer demand, inventory, timing, and competitive shifts. That’s where PROS first pioneered the use of AI to cluster similar transactions and predict optimal pricing outcomes. From there, we expanded into other industries, manufacturing, distribution services, bringing that same level of precision to more complex negotiated b to b environments.

Today, SPO supports both negotiated and nonnegotiated pricing, helping organizations across various industries make sense of fragmented data and turn it in into intelligent winning prices.

When we talk about the impact of smart price optimization, it really comes down to three key themes.

These themes are a value economic, operational, and strategic.

Starting with economic value, SPO enables companies to analyze historical performance, track seasonal patterns, and respond to market conditions with agility. That means you’re not just pricing based on instinct. You’re identifying precise opportunities to grow revenue or protect margin.

Next is operational value. By enabling more self-service and reducing the time it takes to quote, SEO empowered sales team to act faster and with more confidence. With intelligent guidance in hand, they can offer the right price to the customer at the right time, increasing both win rate and efficiency.

And finally, there’s the strategic value. SPO leverages advanced AI to model price sensitivity and elasticity at scale. This allows pricing teams to shift from reactive tactics to proactive tactics and support, the long term business objectives.

So whether you’re focused on short term margin wins or long term transformation, smart price optimization helps you get there with science, speed, and precision.

Okay. So the foundation of price optimization is really your data, specifically account, product, and transaction data. So that includes everything from account level attributes to purchase history, product details, and competitive context.

That data feeds into our AI models, which are trained to uncover patterns and relationships, like how different customer types respond to price changes or which product combinations are most price sensitive.

Now the way that we turn that data into pricing guidance depends on the type of AI model being used. At Here at PROS, we offer different flavors of AI, each tailored to different pricing needs and business context.

Some organizations prefer segmentation based models, which group similar transactions to generate precise pricing by cluster, while others lean into the power of neural networks, which detect patterns and nonlinear relationships across a broader range of data.

Both are powerful, and choosing the right approach really depends on your data, your pricing strategy, and the level of precision that you’re aiming for. So I’m I’m gonna take a closer look at each of these flavors, starting with dynamic segmentation. So customer data can vary widely. Even two transactions from the same account can look completely different depending on the timing, volume, product, or region.

So how do you make sense of all that variability? That’s where segmentation comes in. By identifying patterns and how similar products were bought by similar customers under similar conditions, the model can recommend the optimum price for each new deal. First, by creating clusters of transactions, grouping them based on shared characteristics like customer profile, product type, or buying behavior.

Then we analyze the pricing patterns within each segment and generate a guide guidance envelope, your floor, target, and expert prices, specifically tailored to that transaction.

This segmentation process ensures that the price is anchored in logic and relevance, not just overall averages or outdated assumptions. It’s especially useful in negotiated pricing situations where context matters and nuance wins.

The second science approach we offer focuses on pattern recognition at scale. This method is ideal when your data is more complex or highly varied. Think large product catalogs, global customer bases, or rapidly changing demand.

Instead of clustering transactions into divine segments, this model scans across all the available data, customer behavior, product interactions, deal history, and detects deeper patterns. It can capture subtle, nonlinear relationships that wouldn’t surface in traditional analysis.

This allows for much more granular and specific pricing recommendations even when the data doesn’t fit neatly into categories.

What really sets this apart is the level of visibility it provides. For each recommendation, you can actually see which attributes and the signals have the most influence on the recommended price, and those can vary significantly from one quote to the next.

This kind of attribute level explainability helps build trust, especially for pricing teams that need to justify pricing to sales leadership or customers.

So whether you’re using segmentation or neural networks, the results are clear. Our customers see improved win rates and higher margins with tighter control over pricing consistency. But just as important as the outcome is the experience, And that’s where I want to spend some time showing what it looks like in product.

So as we move to the explainability section of this solution, this screen answers the fundamental question, what’s actually driving pricing in your business? What you’re seeing here is the attribute analysis view, which breaks down the most influential factor shaping pricing guidance across your historical transactions.

At the top, the attribute significant chart, which ranks which attributes, like product group, geography, or customer spend, had the greatest influence on pricing across transactions. The longer the bar, the bigger the impact.

Below that is the attribute value impact chart, which takes a closer look at one of those attributes. Here, looking at region, it shows how each region pushed price prices up or down along with the transaction volume for context.

At some point, you might be wondering, how do I know if the model is doing a good job? This screen is all about trust and transparency.

On the top left, you’ll see accuracy scores that measure how well the model predicts both customer specific and peer predictions.

These scores give you confidence that the model understands your data and is tuned to your pricing strategy.

At the bottom, this is where you can validate what the model is recommending aligned with your historical behavior.

Overall, the point of this view is to help you assess the health of your model and build confidence before rolling out pricing to the field.

And the other screen I’ll show off is how the system arrives at the recommended price. At the top, we show the recommended price envelope, floor, target, expert so users can understand the range of acceptable prices, including the one most likely to win the deal at the optimal margin. Right below is the win rate chart, which visualizes how price changes affect both win probability and margin. It helps users quickly grasp the trade offs between price and performance and reinforces why the recommended target is the best choice.

To build trust in how the model got here, we include an attribute waterfall chart, and this shows how each attribute, like product group, channel, or region, shifted the prediction from the baseline to the final recommendation.

Together, this screen makes the model’s logic transparent, enabling pricing teams to not just see the what, but also understand the why behind each recommendation. So, Frank, I know you’re absolutely essential to all these analytics screens that we’ve built out in the product, so I’d love to hear how, Wilbur, Allison, you specifically are using with some of these screens.

Yeah. You know, I think you leaned into it. The explainability was the key for us at Wilbur Ellis. As we moved away from the pricing spreadsheets, we initially launched the dynamic segmentation model, with with great success, But we knew there’s some limitations. So as the the next generation of the neural network model came out, getting a seat at the table to kinda get a a a preview of what was coming, we really asked and challenged the PROS team for the explainability.

And these waterfalls really allowed us to to have that why and help give credence to why we’re given the recommended prices we’re given on the output.

And, you know, the the power of this and even if you go back a screen, to the the model help, allows you to kinda be that pricing doctor to truly get a feel for how the patient is, that patient being the model.

So you know if you need to go in there and and make some tweaks or or even have the ability to add in some additional attributes. And that was really for us one of the kind of the lunch pins of why we moved to it was the ability to add additional attributes right, within this model.

But, also, we were able to self serve a lot of this. So we moved from the the prior generation to the current generation, within a couple weeks, and we were I always like to say we were under cost and and ahead of schedule to move to it, but we’re able to do it ourselves with just a little bit of, support from the PROS team. But, but, yes, the the explainability here within this, the waterfall charts, really allow us to, give the the sales leadership trust in how everything is being made. It’s not a black box solution. It truly is something that’s powerful and explainable.

Awesome. And I’ll hand off the baton to my colleague, Ben.

Let me stop sharing. Here we go. Perfect.

Okay. So, thanks, Matt. We were just talking about the optimization half of smart price optimization and management.

And so that’s what we this portion of of the diagram here, and now we’re gonna talk about the the second half of smart price optimization and management, which is the the management half of the, the, the diagram. So when we think about smart price management, really, it’s it’s a price management execution tool. Right? So we we handle everything from pricing and strategic discount list, pricing rules, helping helping our pricers, govern sales orgs by by offering and managing guidance and restricting their sales behavior into, targets that are in line with with the where the pricing organization is trying to steer the business.

The execution side of the house, we we we support just in case and just in time pricing. So you can think of, like, price books to bring those books to to ERPs or directly to consumers, distributors, as well as the the dynamic pricing side of the house, which allows, our our customers to take over from a kind of sluggish price resolution operations of point of sale and pricing agent systems.

So it covers a lot of ground, but we’re gonna focus the conversation today on to three kind of areas in where price management helps support the optimization side of the equation. So we’re gonna focus the conversation on, conditions or business rules. We’re gonna focus it on the the very those price points as well as how we measure the evaluate success of the optimization.

Let’s talk about business rules first. So if we take a look back at this diagram, you see on the far, left hand side, this represents the the outcome of the optimization. So in this illustration, we’re looking at a floor target expert, so seven seventy six, seven fifty four, and seven forty seven, and and that’s great. So as Matt kind of alluded to at the beginning of the presentation, we start entering into the human loop phase of this.

So the opposition is great, but we also want to make sure that it takes into consideration data that may not exist in the dataset that’s being evaluated, as well as the business intelligence directives and goals of a broader pricing organization with the private business as a whole. And those business rules, can consist of different different kinds of things. Think, kind of minimums. So making sure that making sure that the cost is is our absolute form, maybe a relationship to cost.

So maybe it’s not the actual cost, but a distance from cost is where our absolute minimum needs to be, as well as upper boundaries by customer or customer groups.

You could be working hand in hand with product organization that has margin minimums that we wanna make sure that we are enforcing across, the the organization, ensuring that that the optimization, adheres to, as well as rationality, more complicated kind of business role where we’re thinking about different channels, different price points, and making sure those various price points are in line with other pricing and across the organization.

So when we think about business rules, we think about it in terms of two practical features within smart price management. Think about it in terms of the lookup, which is essentially a a mechanism to house the constraint data for the optimization. Lookups are used for a variety of reasons across the broader price measure ecosystem.

But for the optimization side of the house, think of it as where that constraint data is going to live. So where are my costs? Where is the relationship to my cost? In this illustration, it’s a guidance multiplier. So the the nature of the this constraint dataset is organized by one or potentially more hierarchically based dimensions.

So in the case of this illustration, we have channel, geography, and product.

And these dimensions are are can be multiple level. So I can store information up a product hierarchy so I can decide what that factor needs to be for my product lines or my product categories or my SKUs, as well as geographically. I can store it at a store or region or country, whatever the hierarchy allows me to store. And so what that allows the user to then maintain is not a flat list of of factors.

It’s a an exception based constraint matrix. So I can store my constraint data up and down the hierarchy so I can manage those exceptions very clearly.

Now the second part of the equation is something that we call pricing methods. Pricing methods is the the logical backbone of the growth platform as a as a whole.

So this is where those logical operators, those conditions are are managed. So I take that constraint data and and tell the the agent how to apply it.

And that can be as simple as the illustration in this in this screenshot here as applying that factor to the raw output of the optimization, that target price, it can be as as complicated as I have a dozen different constraints that I’m trying to manage, and I need to ensure that they’re they are applied to that raw, optimization value in the right sequence in the right way. So the price method, you can think of this very not too dissimilar from the function library that’s offered in Excel, but we have a few more advanced functions that allow us to do a little bit more complex things at scale.

So again, these pricing methods can be as complicated or as simple as the situation demands.

Frank, I did wanna ask you, so business rules, so getting getting your users in in the middle of the optimization, can you talk a bit about how WebRallis is is thinking about that and where where it made the most sense for your business?

Yeah. Ben, thanks. I I know that human collaboration layer here with the business rules has been cut one of our keys to success, where we have a really strong model, but then being able to layer in different business rules, cost adjustments. And if we all rode the roller coaster in twenty one and twenty two where costs are going up, down, and sideways, but to be able to put in some minimums. Right? So that we know that, the optimization model is adjusting when we have large scale cost changes that we probably haven’t seen in quite a while.

Being able to, kind of wrap some of our our products together so we have, you know, that brand ladder approach to ensure that, while our sales team may not all have be rational on how they brand ladder, but at least our output is, being able to do that.

But the lookups themselves are very powerful. Being able to add ad hoc data right to the to the to the model or to the dataset within the management solution is is powerful. But then also updating the the pricing methods, formulaic pricing approach for us has been very powerful.

And the fact that we can do it ourselves and self serve that has been powerful. You know, I’ll I’ll give a shout out to the PROS University side. The training side that that PROS offered to us to be able to kinda skill us up in this area has been powerful itself as well.

Thanks.

Let’s talk about the second, aspect of price management that, helps the optimization.

Let’s talk about the delivery. So when we think about this at a at a at a high level, we we think about this in terms of two modes. There’s the real time delivery, over here on the left, where you think about this this consumer of the the price being, an end consumer that is logging in to some kind of third party application. Think of it as a as a quote or a ecommerce site, and they are deciding what they need to either quote, negotiate, or they’re deciding what they’re trying to order. And that site allows them to peruse some form of a catalog. And, ultimately, once they land on a a product or a group of products, the the third party site needs to say answer the question, what is what is the price for this particular circumstance?

And when we’re dealing with a real time integration, those third party tools will then make a request out to PROS.

And the the the pricing that is is handled in PROS, we’re not thinking about in terms of giant price list anymore. We’re thinking about in terms of this pricing percent over here is not managing a giant price list. They’re they’re managing rules. They’re managing components. They’re managing costs. They’re managing all the the various little bits and pieces that come together to form a that net price, that actual pricing circumstance that needs to be arrived. So then when that request comes in, PROS computes and derives what that net price is based on the unique circumstances that are being requested and delivers it directly to that third party tool or application.

The other mode is batch delivery. Batch delivery starts off largely the same way. You have that user that’s logging into a a third party tool, that quoting that quoting tool, that the ecommerce site. Right? And they land on that product, it peruse it, and they decide, okay. What’s what’s the right price? And that question is imposed to a more centralized brain, typically an ERP.

And that ERP is going to house all of the pre computed prices and and various rules that it needs to come up with, the the net price for that circumstance.

And that series of list prices is then is is managed by that pricing persona within PROS to form a more comprehensive list price, a more comprehensive group of pricing to where every price point that I care to offer is pre computed upfront.

That usually means that there’s an approval process involved. We wanna make sure that that the human the businesses that that decide to go this route usually decide they they they want a human in involved or our systemic rules involved in every price that goes out the door. And so those prices are pre computed, approved, and ultimately passed into the centralized repository, which again is typically an ERP.

Frank, let’s talk about Uber ELLIS and and the delivery mode that you guys ultimately decided on and and kinda why that fits Uber ELLIS and and what kinda led you to those decisions.

Yeah.

Yeah. We we leaned into the real time delivery. Being able to call that pricing in real time, apply business rules in real time was a key advantage for us.

And also just because we don’t know of every permutation of what customer product combination we’re gonna price at in any given time based on what economic solutions we’re trying to solve for. So being able to call that in real time, rely on the business rules being applied, being able to call back to the optimization engine to pull in that pricing kinda gave us an advantage versus trying to to batch push every customer product combination into our ERP. So, we really went into that cloud based platform, being able to have pricing go out to multiple systems, systems, if it’s directly in the ERP, if we’re placing order there, if it’s in one of our own UIs, or even a customer portal. We’ve been able to pull in and and kinda have control through those business rules, by being able to call, via the API.

Great.

Alright. Finally, let’s talk about measuring success. And there are two ways that we’re gonna talk about measuring success, today. The first is, self-service.

So we want the users of PROS to be able to evaluate their own success. And we we do that through a module inside of Smart Price Management called insights. Within Insights, the users are empowered to build their own dashboards, build their own charts, build their own workflows to arrive at an actual result. This is an illustration of an adoption performance workflow where the user starts out in a sales dashboard where they can evaluate KPIs, see how sales have progressed, see how closer and far in aggregate we are getting to the the envelope for target expert.

We can then drill into regions or sales reps to evaluate individual performance or our territory performance to understand how close, how often are we actually hitting below for? How often are we hitting above target or above expert? So we can have quantitative information about how how readily are we using the science that is being proposed during the negotiation cycle. And so so you as a price, you can have very real conversations with the sales organization with a very specific aspect or person or or manager or territory in that sales organization about why.

Why why are we pricing above below four? Why are why are where’s the struggle? And and and is do we need to, add some more constraints to science to make it more realistic, or is there a lack of confidence from the sales organization that we can help alleviate by opening up some some reports and and showing them the why the science is recommending, what it’s recommending based on, the peers that that we’re seeing in place. So we can have a very productive and informed conversation with that sales organization.

The second aspect of the managing and and understanding success is a PROS governed program that we call the customer adoption program. Inside of the customer adoption program, the it’s the the other side of the equation where PROS is taking a look at how how you are leveraging the system. How how are we measuring towards pricing towards the envelope and understanding how are we progressing towards a kind of a price what we’re calling a pricing discipline. And that’s kind of the level of of where we’re pricing within the envelope. And having a conversation with your pricing leaders, your pricing champions to say, this is what we’re seeing. And these are areas that we think there might be opportunities for us to, make slightly different decisions or have more coaching conversations with our sales organization.

And and really have a third party come in and and have a a real productive candid conversation with the rising champions at our our our customer sites to to help have a a back and forth dialogue on what we’re seeing and what they’re seeing and maybe the why behind it a little bit.

Frank, can you talk a bit about your experience with with insights, with the cap program, and and how that has has worked with the overall estimate?

Yeah. Absolutely. I, you know, I think the the actual insights we can garner, right within the solution have been impactful.

It it really allows us, to drill in and understand where we have opportunities to have discussions, with, really, what is it worth. Right? What how much is it worth to go after these, and have these discussions, and really be able to push that data?

And then on the other side with with CAP, as you mentioned, that customer adoption program, getting that outside perspective from our customer success manager kinda gives us, some additional credence to go back to to leadership and say, hey. We have some opportunities in certain areas.

And maybe we’re a little selfish, but we’ve been able to kinda pull in the price discipline index to Wilburillo’s vernacular now. I I know our CEO will ask me on an update, almost weekly of how are, what’s our price discipline index trends. We break it out, within our geographies to kinda understand and kinda rank and almost gamify those numbers to see are we growing, are we sideways, where we have opportunities.

So we’ve really been able to kinda rally around that price discipline index, and look at it, again, by geography or even drill in kinda over time and see how it’s changing and how is that trending with our margin gain. Right? So, our price discipline index is going down, but our margin gain is going up because we’re we’re changing products, but we’re not quite pushing the envelope, so to speak. So I think it’s been both sides have a lot of positives to it. And then you really have a third analytics dashboard when you’re looking at your own internal data. What is your what is your overall financial piece? So, really, it’s it’s almost three different sides to to kinda take, actionable insights into.

Great. Great. Any closing thoughts or remarks on Wilber Ellis’ experience?

Yeah. You know, we we’re able to tell our story at the last outperform conference that PROS hosted in Las Vegas, but, you know, we’ve been able to really help us transform our business. We launched in twenty twenty, so right right in the height of COVID, we were able to unlock, our segmentation based price optimization.

And then in twenty twenty three, we’re able to move to the neural network based kind of the next generation of of modeling to drive even greater precision. So so, again, as we look at our model health, we’re able to continue to add in new attributes and, continue to evolve our pricing, but really move away from that cost plus to really a market focused, pricing.

Yeah. We’ve seen about two percent margin uplift right as we launched, and then that the payback from moving from the the dynamic segmentation to the neural network was even quicker just because, of the, being able to really self serve and and change that.

Again, we noticed the ability to to remove that segmentation, from the attributes and just really let the attributes shine has been a a positive for us. So, again, continue to enhance, and more accurately predict where prices, should be.

And, again, I think the elasticity plays into a lot of that too where we can actually see if, okay, if we do lower the price, what is that potential, uplift, or what does our win rate curve look like when we’re trying to price out our between our floor target and and our expert within that price envelope. But I think it’s been a you know, it continues to be a a a journey, but I think it’s been continue to be a great partnership between ProZ and Wobrales as we continue to unlock, additional capabilities.

Great.

Okay.

We’re gonna move into some q and a. It looks like we have had some questions come in while the, we were we were talking here.

Brian had a question about our current or future plans for European integrations, and it’s it’s a great question.

Integrations data integration data management in general is a large focus for PROS.

We are making continuing to make investments towards better integrations, better ways that data can come in and out of the PROS ecosystem.

We are investing heavily in connectors and integrations.

We have a a team that’s been purposely, built to focus in that area.

We know we are we will be focusing fairly heavily, from an ERP perspective on SAP and and SAP.

There’s a lot of variations of SAP. So, we’re we’re gonna be focusing and and strategizing towards a few aspects of SAP. But we we do know that we have a desire to move into the Microsoft ecosystem as well to have better support, integrated integrations for up and out as well. It’s a great question.

Another one, I think, is a a question for Frank, about what lessons have you learned or best practices that you can kinda share from whoever else is kind of pricing transformation learning.

And I mean, it looks like it’s a two part one. And how long did it take over to transition from gen three to gen four? Maybe you touched on that a little bit. Can you expand on that?

Yeah. So I I think lessons learned and and best practices is a, you know, data, data, data. Right? So the the the cleaner the data, the the better the outcomes.

So we we would take a lot of time to kinda we did take some time to clean our data, but don’t think that the case we learned early on is find find your change champions. Right? Those local advocates that can help you, in that journey as it is a shift. Right?

You’re you’re moving away from some of the local at least our end, We moved away from the locally managed pricing to more of a centralized pricing output, but still having the the the local team champions with the with the big deal. So change management and communicating the change and the why, and then, obviously, offer c suite, involvement and their ability to support the project, are a big deal. Right? We’ve had some really good sponsors, locally and what else to help us push those those changes.

I think I mentioned it, but I think to kinda talk through the transformation journey or transition from the dynamic segmentation model to the generation four neural network model.

When we launched it, within, you know, once the prod team made the configuration change to unlock that capability, we had a usable model within three days.

So we were able to kind of pull in what attributes, layout, make some some initial changes. But within three days, we had usable data.

And then it was just, you know, then it was just the normal testing out, do some regression testing on on pricing.

We did something that they don’t always recommend, but we did compare models. Again, it wasn’t that we were looking for the same results, but we wanted to see what those minute changes do look like.

And we found some I’ll say, I’ll call them unicorns with the dynamic segmentation model where we couldn’t solve using a lot of the attributes. So we’re able to pair those into the the neural network model and see what attributes were having influence on those prices. So that but, again, we were a three week total journey from kind of start to finish, but within three days, we had a usable model.

That’s great.

Does Palm work well for b to c? Great question.

So b to c is is fundamentally about scale. Right? We’re dealing a lot more volume of of price points and and customers than than b two b typically.

And PROS is built for scale. PROS is built for scale. So we we do have a number of b two c customers.

Matt, do you have a a thought on I think this question is kinda twofold as far as, like, pricing, just being able to to execute price pricing. So the science bits of it, does does that work well for b two c?

Yeah. We have a pricing model. I alluded to it a little bit. Was that for non negotiated scenarios, so that’s all about, you know, take it or leave it price.

You’re putting that price out there. It’s really the sign the way the science is working is is based on demand and volume. So as, you know, demand goes higher, maybe you wanna increase the price and the the model will learn from that. Or as demand goes lower, you’re selling less volume, maybe you wanna decrease the price.

So that’s really built for that b to c, ecosystem.

What steps were involved in transitioning from spreadsheet based pricing to AI price guidance?

Yeah. I’ll take that one. So, obviously, it wasn’t just rip the Band Aid off. Right? It was really about the training, and showing the sales team kind of early on in the testing phases of what the the negotiated price points would look like.

We had a lot of communicate you know, we had a full fledged communication plan of what is a pricing envelope and how are we gonna, you know, utilize those for price negotiations versus, you know, moving away from a static price point, that may or may not have been negotiated around. So, it it’s all about the communication, but you do have to have, plans of how do you take away those pricing spreadsheets so people don’t continue to refer to, well, the price used to be x, now it’s, you know, y y plus an envelope. So you do have to have a a transition plan to to really take those out of the the place, but the communication ahead of time, the training, the showing that the really, the showing and telling, help us transition.

I think Frank’s still here. Yep.

Yeah. I think we lost audio for a second. Yeah.

I think it looks like it might be a bit choppy.

Gentlemen, thank you so much for your time. Frank, I’m I don’t know if you can hear us.

But was there anything else you wanted to share? Maybe something that didn’t make it into the presentation deck or just any other information that you’d like the viewing audience to know before we go today?

No. I, you know, I think we really hit on it, but I think the explainability, the ability to champion that change as we’ve been into it and embrace the AI, I think it’s, you know, it continues to be another tool in our toolbox for for the pricing team as well as the sales team. Right? So this isn’t about the machine being in control. I think we talked about it, during Ben’s part of the human collaboration layer that we’ve got with the management side. But, really, the the two, elements that we’ve utilized within PROS has helped us be better and more rational in the market.

Yeah. Perfect.

And as we kinda said, I mean, these these pricing problems are are not unique, and they are are are long lasting. And and if you guys are interested at all in understanding how PROS smart price optimization management, any any additional questions or want a personalized demo or or some kind of consultation, just reach out. Our I I believe our emails are are, part of this. So feel free to reach out to PROS, and we can talk and at length about this stuff.

Alright. That sounds great. To all three of you, Matthew, Ben, Frank, thank you so much for your time today. Thank you for sharing your information.

We hope all of you viewing from your desks, from your homes, wherever you may be today, got a lot of great info out of this and are inspired to approach your work a bit differently now.

Certainly, feel free to connect with PROS on LinkedIn as well as professional pricing society.

On behalf of both press professional pricing society and PROS, we thank you all for being here and wish you a great remainder to your day.

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