Margins are shrinking and deals are more complex than ever. Legacy pricing tools can’t keep up. Join industry experts for a fireside chat on how AI is transforming deal-making, helping you uncover margin blind spots, optimise rebates, and make smarter, more profitable decisions in real time.
What You’ll Learn
- The Margin Challenge: Spot hidden margin leaks before they impact profit.
- The AI Advantage: Predict willingness to pay, forecast deal profitability, and recommend optimal rebates.
- Real-World Scenarios: See small changes create big margin impacts and how AI prevents surprises.
- From Vision to Action: Practical steps to build an AI-enabled deal strategy and future-proof your margins.
Speaker
Nick Boyer
Senior Director, Strategic Consulting, PROS
Full Transcript
What’s up Internet? Jeff Gretler here, head brand ambassador of Spiceworks, and we have a great show for you today. I recently got to sit down for a fireside chat with Nick Boyer. He’s the senior director of strategic consulting EMEA for PROS. It’s a great conversation, so check it out right now. Nick, how are you doing today?
Yeah. I’m well. Thanks, Jeff. How are you?
Good. Good. Everything’s going okay on this side as we were as we record this. Am in Austin, Texas. And where are you located?
So I’m located just outside Nottingham in the UK.
Excellent. Excellent. So we are we’re in the fall in here in Texas, which is basically lighter summer.
Was gonna say the fall for you is probably summer here.
Right?
Yeah.
It’s summer.
Ten degrees here today.
Even though it’s fall, I was walking around in a, you know, a t shirt and shorts last night, and I was like, this doesn’t feel like the fall season. But here we are. That’s that’s Texas fall.
That’s beautiful. My family is actually from Texas, so I go to Texas quite a lot. This is a nice time of year to go. I’ve tried a lot.
Yeah. Exactly. This is this is it where you don’t have to worry about any triple triple digits Fahrenheit.
Yeah. Exactly. Yeah. Humidity.
Excellent. Excellent. So, you know, talking about this topic today, you know, Nick, what makes this topic relevant as of today in twenty twenty five?
Well, I think for so I’ve in kind of the pricing space for fourteen, fifteen years now. And during that time, the focus on improving list prices, improving discounts As companies have given that more and more focus over that time, they’ve created pricing teams to really put a lot of attention on optimizing the list price and optimizing discounts.
It hasn’t really been the same attention on rebates. So it’s kind of I separate on invoice discounts versus off invoice concessions like rebates, basically.
And they’re not as visible to companies, but they can erode margins by up to ten percent and kind of fly under radar as a result, it’s kind of like a margin blind spot.
And I think, you know, a lot of companies are getting to the stage where they’ve sorted out the initial problems with pricing. And now they need to look at the overall picture and make sure that the rebate side, the off invoice side is also optimized in the same way.
Awesome, awesome. And we hear obviously a lot about AI today, you know, and an AI advantage. How can AI create a new era of that margin intelligence?
Yeah, well, so AI is really important for a number of reasons around the rebate space.
The first thing that’s required is for a salesperson who’s negotiating a deal to be able to see the total picture. So how much are they proposing to give away on invoices versus how much they’re proposing to give away off invoice.
And that’s something that’s lacking with a lot of companies at the moment. Those two elements of a deal tend to be managed through different systems that are not necessarily connected. The off invoice concessions or rebates are often done in spreadsheets even so and even the time in which those are negotiated with customers are different.
So the first thing is bring them together into one solution. And then AI can drive optimizing both. And we can go into that in a little bit more detail later in terms of how AI can help with that.
Awesome. So and so I’m assuming what you’re referring to is the margin problem. Like, are people losing their profit?
Yes.
So the issue is with these siloed systems and having pricing and quoting tools that sales guys have access to, but not having rebates in those solutions.
So that when deals are being negotiated and being approved by a salesperson’s managers, the rebate element is not visible.
And research shows that one point three percent of revenue on average across industrial firms is leaked. So one point three percent of revenue is leaked through rebates. And that often goes unnoticed. So that’s kind of what we call the margin blind spot.
And that’s kind of what needs to be addressed. Rebates are meant to drive loyalty and growth. But they often just become extra discounts. So that’s why it’s really important to have a more strict strategic approach to rebates and make sure that they’re visible when when the deals are being negotiated and approved in the same way as the on invoice discounts.
Yeah. Let’s get a little bit then into the margin problem. Like, where where are people losing their profit?
Yeah. So there’s a there’s a number of things around about around rebates and how that impacts the the margin leakage and resulting in losing profit. So first of all, rebates are often not treated as strategic, but more tactical. So you know, when speak to sales teams, or when I speak to pricing teams, they often blame the sales teams for using rebates as a way of getting around the focus that is on invoice discounting. It’s the on invoice discounts that tend to go for approval.
It’s the on invoice discount, where there’s a lot of guidance from pricing teams, but there isn’t with a lot of companies the same attention on rebates. And therefore sales teams use the opportunity to offer rebates as an additional discount, that’s kind of a little bit off radar. That’s not really how rebates should be used. Rebates should really be used to drive desired customer behaviors, right? So we’re either using rebates to generate more volume from our existing customers or to generate more growth or to generate loyalty or to improve the product mix. So there should be a strategic reason around why we’re offering a rebate.
And there should be a governance procedure around the sales team to make sure they’re only offering those rebates for those strategic reasons. So that’s kind of the first reason why rebates are a problem in terms of how they erode margins. And then the second problem is deal complexity and rebate complexity.
So you know, a lot of research has been done. And on average, there’s four or five different rebates that tend to be offered to a customer. And they’re kind of multi layered, if you like.
As a result, each layer adds on kind of nonlinear effects and they therefore become hard to model manually. So for example, the first element of the rebate could be if you go over one hundred thousand dollars we’ll give you an additional two percent. Then there’ll be an additional rebate around, if you buy from this product group, x quantity, then you get an additional one percent. And then layers like that, get added on.
And the cumulative impact of all of those is very difficult for a salesperson to calculate. And it’s very difficult to take that into account when approving the deal. So they therefore kind of go off radar a bit and kind of result in margin erosion. The total deal value discount plus the impact of the rebates is kind of rarely calculated accurately.
And then the final problem is that legacy solutions like ERP, CRM, Excel treat rebates and pricing separately. So they’re in silos. And therefore, when deals go for approval, a manager cannot see the total impact of the deal across the on invoice discounts and the off invoice rebates. And all of that ultimately results in putting the margin at risk and margin erosion.
All right, Nick. So let’s get into some real world scenarios and some examples. What does AI driven margin optimization look like in practice?
Yes, so in practice, if we take an example of, if we think about a deal, and that deals got a rebate on and I’m a salesperson negotiating a deal.
If you maybe take a hypothetical scenario of a customer price of one hundred euros and cost of goods sold of fifty five euros, And that customer is currently giving me just under one thousand in terms of quantity in terms of volume. And I as a salesperson want to encourage ten percent more volume, I may think about offering you know, a five percent rebate, so they get an additional five percent if they can give me over one thousand units of volume.
But the reality is, as a salesperson, it’s very difficult for me to calculate where is that breakeven point. And if you take that example, the breakeven point is actually eleven forty two units. So it’s very difficult for a salesperson to manually calculate. First of all, where should the rebate structures land for it to be to make sense and for it to be a win win scenario, it’s very difficult for them to do that in real time. And it’s also very difficult for them to think about the cumulative impact of the rebate programs that I’m proposing and the on invoice discounts that I’m also conceding as part of the customer negotiation.
So AI can be used in a number of different ways to help with that. So first of all, AI can predict the overall willingness to pay. So what is the total margin that I should be expected to be able to negotiate with this type of customer for this type of product?
And the AI will tell me that willingness to pay is going to be different depending on the size of that customer, depending on how much of that particular product, that particular customer has bought from me in the past, and therefore how important that product is to that customer.
And it will take all the attributes associated with the product and the customer into account and tell me what’s driving the willingness to pay. And what is that willingness to pay in terms of the margin I should be making on that deal.
And then it will also tell me if I try and increase my margin for that particular customer, what is the probability of me losing that deal? And guide me to a sweet spot where I can increase the margin sufficiently without a material risk of losing the deal.
So that then gives me a target of well overall, where should this deal be negotiated at.
And then the second thing it can do is it can recommend to me what the optimal rebates should be.
So there’s now something called agentic AI, where agents can help a salesperson in real time model the rebate structure. So what should the tiers be? What should the thresholds be? What should the incentives be? So the sales team can vote, the salesperson basically narrate to the AI agent. And in that scenario, talked about earlier, you know, if I want to give a growth incentive for this particular product, where should I land at and the AI will automatically create a rebate for me, which lands above the eleven forty two units to make sure that it’s a good deal.
So really to take the pain out of having to calculate all of these cumulative impacts and make sure that I’m landing in an area that kind of reflects the customer’s willingness to pay, but also gives me the profitability that I need.
And then finally, AI can help actually forecast that rebate performance. So once I’ve negotiated those rebates, and put them in place with my customer, it can forecast on a month to month basis, if that rebate is say over a twelve month period, two months into the agreement, where am I forecast to end up? So if done a rebate that’s looking for an additional one thousand units, how likely is the customer to buy those one thousand units from me? How is it trending? The AI can help forecast that.
And then I can use that in my management accounts and in my general kind of management information to understand the overall profitability of my customers.
So and now I will return rebates from kind of static paybacks into kind of dynamic levers for profitability.
I think there’s an intimidation factor that happens when it comes to implementation and getting AI in there properly to use it to your advantage like you were just talking about. So what would you say are the key steps to successfully deploy AI for margin optimization?
So key steps.
First of all, it’s obviously important to get the data right. So AI uses training, uses sales transactions basically, as training data. So it’s important to make sure that that data is obviously factually correct. And also that there isn’t any hallucinations in the AI caused by the data.
So for example, an obvious example is you wouldn’t use any data that describes the current sales organization, you take all of that out of the equation. So the first thing is to have clean data. And then the second thing is to make sure that the AI is explainable. So that you can explain to the sales team how the AI has arrived at its conclusions.
So having some graphical UI available to the sales team so that you can look at different product customer combinations.
And the UI will tell you the attributes of the AI feels drives the customer’s willingness to pay in this specific scenario, to help the sales team get a better feel for the logic, the reasoning that the AI is using and give them more of a sense of trust that the prices of the AI was recommending is going to be something that they’re going to kind of agree and buy into and therefore will start using and that will then drive their user adoption and that kind of acceptance of that UI.
And then finally, you really need the ability to deliver that into the sales team in a very efficient and easy way so that the sales team is willing to use that AI. So you know, ideally, into their quoting solution so that each line in the quote when they enter a product, you can deliver that AI guidance, those AI recommendations straight into the quote. So it’s very easy for them to use the recommendations that the the AI is providing.
Right. And that, again, when we’re talking about intimidation, we have to make it as easy as possible to get people to buy in, right, to to use this to their advantage.
Yeah, exactly.
It’s a buy in to trust it in the first place and then make it as easy as possible for them to use. Yeah, exactly.
Awesome. So as we start to wrap up here, let’s talk a little bit about some of the strategic takeaways and future outlooks.
What is the future of intelligent deal making? And how do we get there?
Yes, I think the next kind of competitive frontier, if you like is AI embedded commercial decision making. So that’s having an IT infrastructure that can bring all these different elements of a deal together. And there’s, I think there’s three elements that we’ve talked about. So first of all, is having the quoting solution so that the salesperson can compile the different products that they want to offer to the customer, and have the AI recommendations delivered into that quote. The second component is having a rebate management solution so that the salesperson can define the rebate programs that they’re proposing for that particular customer. And the rebate management solution can calculate the impact of those proposed rebates and surface that in the quoting solution.
So that is then a full picture of all the margin that’s supposed to be given away to that particular customer. And that’s then used in the approval workflows to decide whether to go with that deal or not.
So it’s really about having the integration that the on invoice and the off invoice into one solution, And having AI drive recommendations across both the on invoice discounting and the rebates that we’re going to be proposing so that we can optimize the total deal and not just the on invoice discounts. Companies having a master intelligent deal maker that kind of turns rebates into a profit lead and uses them strategically and measures the impact of them on margin.
Yeah, I mean, and it sounds like, you know, once this is configured correctly, it sounds like the value is gonna be a steep up into the right once you actually get this configured right. Right? As far as, like, the investment versus your return investment, if done properly configured properly, it could change the game.
Yeah, I think it can say that one point three percent we talked about this basically bled away through rebates, you can save that. So one point three percent of revenue on average is a lot of money to save, right?
So you get that as one key benefit.
And another key benefit is if you can use these integrated platforms to manage both your rebates and manage your own invoice discounts.
You also have the ability to impose a governance around the rebate schemes that you’re going to offer, right? So in an ideal world, good practice is that I have certain off invoice concessions, and I’m only going to offer those to certain customer segments of a certain size to drive specific strategic benefits. And if I as a central pricing person can control that can put a governance framework in place, which means my sales team can only offer those off invoice concessions in the right context.
And I have the platform in place to measure and understand the margin impact that I’m conceding through those rebates. And I’m using AI to drive them so that they’re structured intelligently.
I stand a fantastic chance of improving my margins and achieving that one point three percent of revenue saving by being really far more strategic around the entire deal instead of just my own invoice discounting.
Excellent. Excellent. Well, this has been great, Nick. But let me just ask you before we wrap up here. So if we were just starting, if I was just your buddy and I came up to you, you know, understanding your expertise, and I’d be like, Nick, where do I start?
Where would what would you tell tell me in one or two sentences?
So I would say you need to start off by most companies need most companies don’t have control of their rebates. That that is the the one area that they have neglected over time. So it tends to be in spreadsheets, if we’re lucky. So the first thing is to get control of those rebates. So having a rebate management solution in place where you can capture those rebates in a structured format.
And then from there, once you’ve got that foundation in place, you can surface those the impact of those rebates into your quoting solution. And you can use AI to optimize out deal. So it’s really starting off with making sure that you’ve got a solution in place to capture those rebates, then integrate them into the overall deal and use AI to optimize everything.
That’s excellent. I love tips like this that will basically just say if you invest this amount of time, you’re going to save this amount of money because time as we know is money. So Nick, thank you so much for your time today. I really do appreciate it.
No worries. Thank you, Jeff.
Alright. Thank you again to Nick Boyer for a great conversation. If you have any questions or follow-up for Nick Boyer or our friends at PROS, check it out right here. Other than that, we’re all set for today. Don’t forget to tech yourself before you wreck yourself, and we’ll see you next time.
