This PROS-hosted webinar, featuring guest speaker Heather Hershey, IDC Research Director for Worldwide Digital Commerce Strategies, explores the evolving role of CPQ solutions in modern B2B digital commerce. Discussion covers CPQ as a strategic asset in unified revenue management, with an emphasis on AI, automation, customer experience, and omnichannel capabilities.
This webinar is a must-watch for anyone involved in pricing, sales, and revenue management.
You’ll learn:
- How CPQ drives efficiency, profitability, and customer experience
- The role AI plays in modern CPQ solutions
- Real-world case studies and best practices
- Strategic insights from IDC’s CPQ MarketScape
Full Transcript
Hi, everyone, and welcome to our webinar this morning, the great CPQ revolution, where we talk about key trends and drivers for pushing your digital commerce initiatives forward with CPQ. My name is Sunil John, Chief Product Officer of PROS, and I’m excited to be here this morning with someone who I’ve come to know over the past couple of years as one of the smartest, most forward thinking analysts that cover the world of digital commerce, including CPQ, but also extending into many adjacent areas that all need to work in concert to drive a digital commerce strategy. Her background in AI makes her insights especially insightful in the context of how AI is reshaping so many industries today. And with that, I’d like to welcome Heather Hershey from IDC.
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Thank you so much, Sunil. Again, my name is Heather Hershey, and I’m the research director of Worldwide Digital Commerce Strategies at IDC.
And you’re gonna get to know quite a bit about my coverage because I think a lot of it is pertinent to today’s conversation, and then you’ll figure out, how we’re gonna tie all these different threads together to figure out how AI enabled CPQ can unlock future horizons of proactive business initiatives.
So let’s move right ahead into that.
Today’s agenda is first thing I’m gonna do is I’m gonna introduce you to my market and the market scape. If you’re not familiar with an IDC market scape at this point, you’ll be a pro by the time we’re done with this webinar.
The second stage of this agenda is the evolution of CPQ and B2B and omnichannel commerce, followed by AI trends reshaping CPQ from predictive analytics to guided selling. So we’re moving from reactive to proactive.
Then, the fourth stage will be the future of commerce, CPQ’s role in unified revenue management, and then finally, some implementation best practices.
But first, let’s go through an IDC market scape. I ran one for CPQ that was published to IDC dot com in, December of twenty twenty four. In full disclosure, I’m currently working on a second one now with two of my colleagues.
But just so you know what these look like, I’m gonna skip ahead just for a second. This is the chart, the famous bubble chart that is at the top of all IDC market scapes.
And, I just wanted to cut to the chase that PROS was a leader in this particular assessment, and, I will explain contextually how that happened and what this actually means. But I wanted to give you an idea of what everybody’s aiming for when we’re talking about an IDC market scape. It’s similar to other sort of documents like a Forrester Wave or a Gartner Magic Quadrant in that, we assess multiple vendors all within the same category of software in this case, because I’m part of the enterprise apps group at IDC.
And we are looking at this in a two by two graph that measures strategies up against capabilities to find the best of the best in breed solutions for a specific software category. So hang tight. We’ll get into a little bit more detail about that in just a moment. But before that, let me tell you a little bit about me.
I don’t wanna dwell on this too much because, I don’t enjoy tooting my own horn, but I just wanna show you that this is my research area. I cover advanced AI impact on digital commerce, which covers a wide range of horizontal technologies, especially tracking emergent artificial intelligence, technologies and embedded technologies inside of the software that impact digital commerce, and then how these particular, new emergent technologies can be leveraged from a strategic point of view. I also cover product data syndications. So that is things like product information management or PIM, product experience management, PXM, commercial marketplaces, sales channel analytics, commerce and shopping experiences, also known as experiential commerce.
And lately, I’ve been talking a lot about product discovery in, LLM or large language model enabled search. So, for example, if you are in Google and you are searching for a particular product and you’ve noticed that you’ve gotten that little summary of generated text at the top via Google Gemini, that is a a bold new horizon of how customers are gonna shop, particularly on the consumer level at this point, but don’t be too surprised if procurement ends up shifting in this direction eventually too. And then also digital commerce revenue life cycle, which is going to be a pretty big topic throughout this, conversation today.
And what I’m really focusing on is how we can offer frictionless payment options to enhance customer experience or CX value and revenue at the same time and scale up, minimize chaos while expanding reach into more digital channels, which is so much easier said than done even under the best of all possible scenarios. But in a world of ubiquitous, persistent uncertainty, it’s critical to keep an eye on flexibility so you can pivot into whichever direction the wind may be blowing while keeping a really firm foundation in your technology stack in the back office for your organizational purposes.
So that’s a little bit about me. Oh, I won, IIAR analyst of the year, Americas twenty twenty four, and I’ve also won, Stevie Awards for marketing in the past.
And this is my market. It’s massive. It has almost five hundred logos in it. Prose is in here multiple times, but there’s so many logos. It’d be kind of hard to spot them, I think. But, you can kinda see exactly how horizontal this market is. And when we are talking about the things that, CPQ touches, it’s a lot.
It’s a lot, particularly in the context of b two b digital commerce. I mean, because you’re integrating with your ERP system, your CRM, maybe a PLM. There there are multiple things that you could be interfacing with and leveraging with your CPQ product, which shows just how critical CPQ can be as a commerce enabler.
So let’s go back to this market scape. What was I actually looking for when I was assessing CPQ for commerce? Well, that for commerce aspect of things is pretty critical to understanding the point of this exercise. In digital commerce, whether we’re talking about b two b or b two c, but, obviously, when we’re talking CPQ, it’s gonna skew a little bit more b two b heavy, though there might be a direct to consumer component, enabled here as well.
When we’re talking about b two b digital commerce, we need you to have optimum agility, flexibility, the ability to penetrate new markets, enter and expand into new territories, to be compliant when you’re doing all of these things, and to be able to protect your margins, to extract maximum positive revenue outcomes.
Again, all of this is so much easier said than done even under the best of all possible worlds. But in this case, I was looking specifically at CPQ products that were modular, meaning that, they have a specific architecture model. Now I’m not gonna get too nitpicky, and I’m not gonna throw you into the weeds in this conversation about, the technical aspects of these different models. But in general, I advocate as an analyst that, buyers look for modular platforms, whether those are fully composable platforms, you know, mock style platforms, which again, that that acronym, by the way, in case you’re not familiar, is m a c h.
That’s going to be microservices. So microservices is is really specific. It’s a really, technically demanding type of modular, architecture build. And then, API first, cloud native and headless.
Now you can be any of those four things, and I’m not gonna get into too much detail about any of them. And, you know, you might not be mock, but you could still be modular. You could still have the kind of architecture that I’m advocating for. So instead of talking about what it isn’t, let me go a little bit more into depth about what it is.
A modular architecture gives you the ability to pick individual discrete packaged business capabilities from a given platform without having to adopt the entire platform.
So it gives you a little bit more flexibility about whether or not you want to enable those functions. It helps you reduce technical debt, and it also helps you incrementally adopt new solutions without having to throw the baby out with the bathwater. As in, you don’t have to completely replatform in order to adopt one of these modular solutions. You can do it a little bit at a time, which tends to ease the change management burden, and it tends to make, the adoption of that technology a little bit smoother over time. It also tends to work a little bit better with legacy stacks. So if you have, for example, an ERP that you cannot replatform off of that is just too fundamentally important for your business, adopting the right kind of modular solution, whether that is mock or a modulith, which is a modular monolith.
If you don’t know, it it’s okay. But if you know, you know, then you can see that you can incrementally migrate from one to the other. So I was focusing very clearly on that in this assessment. So all of the leaders that you see bubbled up to the top of this chart, they all have either, some kind of modular capabilities or they are a modulith or they are mach. So they they will have some something of that nature going on in there or they’re moving in that direction. The The other thing all of them had in common was a strong AI roadmap. So even if there wasn’t a lot of artificial intelligence baked into the product as is, and that is a whole another topic that requires some unpacking to, which I’ll come back to in a moment.
The roadmap for these particular, platforms indicated that there was a significant amount of AI going on under the hood for the future of the product. So they they’re in tune with where I perceive the future of commerce is going as an analyst and what the data bears out.
So let’s talk about AI just for a second because it’s a big, big, buzzy topic, and a lot of the devil is in the details, if you know what I mean. So when it comes to AI, the definition is sort of a sticking point because old school AI, the original definition of AI, could incorporate a lot of what we just consider run of the mill software because symbolic logic symbolic logic, manually configured workflows, those would fit under the umbrella of the traditional old school definition of AI. Though I don’t think any of us on on this particular presentation would agree with that. I think that the goalpost has shifted. And so we have to be careful that we contextually understand what we are talking about when we are referring to AI. Do I think that all AI needs to have reasoning capability? Does it need to be agentic and have agency?
No. I think that machine learning is still very much a a a important part of the artificial intelligence family, and it is crucial. In fact, a lot of what is currently being done in very flashy ways with generative AI technology and agentic AI may actually be better suited for conventional machine learning. And I mean that because when you consider the cost of compute for generative and agentic AI, I think that kinda tilts the scales a little bit in favor of ML, machine learning.
So what all of the leaders that you see here have is pretty robust machine learning capabilities. They may not have the the cutting edge of generative AI technologies, but, again, road map, it may be there. It may be there, that they’re considering agentic AI in the future. But I think they’re all being very, aware and sensitive to the fact that b to b buyers, y’all are a different breed.
You don’t want flashy next big thing, you know, pyrotechnics built into your stack. You want practical things that help you optimize revenue outcomes, that help you streamline operations to get more efficiency out of your enterprise. And so I think that that is something that the vendors in this assessment are highly attuned to. And another thing I I wanna, mention really briefly before I move on to the next, set of slides is that some of the other CPQs that are in this assessment, so the major players all the way across the spectrum, they are also fairly solid CPQs or else they wouldn’t have been in this assessment.
My colleague and I who ran this, we we actually did a series of reports, capture data about forty five, different CPQ products or things that were CPQ adjacent or saying that they were CPQ, whether or not they were. We actually assessed whether or not we thought that they fit the description, and then we, took the top fifteen and put them in this assessment. So to be on this chart, you are a world class CPQ, and that is what you’re seeing here. It’s just that the leaders were the cream of the crop.
So how this IDC market scape for CPQ differed from previous cycles? Well, there was obviously an increased focus on AI and automation, as I mentioned, and the architectural evolution from a strict monolith to modular. So whether that’s fully modular and composed or a modulus, as I mentioned earlier.
Expansion into other aspects of revenue life cycle management or RLM. You’ll see that acronym used quite a bit in this presentation.
And revenue operations, rev ops for commerce. So just a little bit of nitpicky detail here too because, again, I’m just setting the stage for the rest of this conversation so we’re all on the same page here. Revenue life cycle management and rev ops are not the same thing. But if you view them as a Venn diagram, there’s a significant amount of overlap right in the center where the commerce happens, which is why I love these topics.
So revenue life cycle management in general typically is a back office focused narrative. And CPQ absolutely plays a role in this. So, put a pin in that. We’ll talk about that a little bit more in a moment.
RevOps is a little bit more front end focused. It’s customer facing for the most part, and it includes things like sales, marketing, but also commerce. And so, again, they share that in common between the two because you need some kind of conduit for that that money to come in the door. So that way you can do things with that revenue.
So we also focused on enhanced omnichannel potential, greater focus on customer experience because customer experience is a huge differentiator in commerce, and then also enhanced scrutiny of native advanced algorithmic pricing capabilities, which is a pretty big deal. It’s worth getting nitpicky about when you are assessing a CPQ product because, frankly, not all CPQs can handle pricing the same way. Some are pulling those pricing lists directly from an ERP. They don’t bear any influence on the pricing. They’re literally just pulling it in. They’re not pushing data to the ERP about pricing, and they definitely do not have dynamic pricing capabilities. But then there are others who have extremely complex pricing capabilities under the hood with a lot of machine learning and AI baked in that can push and pull that data to and from the ERP and other systems of record, and they are the source of truth about the pricing.
So you really need to ask very granular questions if you are looking for a pricing engine and you’re hoping your CPQ will be that pricing engine. And then there are other vendors like Pros that will offer, a CPQ, and they can also have things like a a pricing optimization platform. So definitely make sure you inquire, ask around about your options before committing.
The evolution of CPQ and b two b and omnichannel commerce. We’re gonna talk a little about the roots of CPQ and new horizons of opportunity for growth.
CPQ’s role in integrating configuration, pricing, and deal management.
So it’s no surprise that modern CPQ centralizes sales execution by unifying configuration, dynamic pricing, and quoting into a single transactional layer.
It provides native systems integrations. Now now take this statement with this with a grain of salt because not all the CPQs in this assessment have a lot of native integrations.
But some of them do, and they will go across a broad spectrum between CRM, ERP, and RLM, which again is revenue life cycle management systems to reduce cycle times and enable accurate rules based deal construction. And that rules based deal construction, that should be a giveaway to you that this is a better use case for machine learning. If you if you said it along with me, if you guess what I was gonna say, you’re right. Machine learning as opposed to something like, large language model generative AI technology.
Modular architectures allow CPQ to scale with business complexity, supporting product services and subscriptions in one platform. And, again, not all CPQs manage subscriptions equally. Some of them do it very well off and sometimes to the exclusion of some of the other capabilities. They may just be very good at subscriptions and not much else, but others are very well rounded, and they can do the subscription management in very complex scenarios that bundle services and products, and help with renewals. I mean, again, ask about the discrete functionality and what can be configured in your CPQ product.
Modular architectures, which I spoke about quite a bit at the top of this conversation, allow CPQ to scale with business complexity, supporting products, services, and subscriptions, but CPQ has evolved beyond that.
They’ve evolved from beyond the roots of a manufacturing tool that was meant specifically for that kind of product configuration to a more strategic role, one where it’s a full revenue engine embedded across industries and workflows.
Now there are a lot of AI trends shaping CPQ from predictive analytics to guided selling, and they can have some pretty significant impacts. The AI impact on dynamic pricing, guided selling, and automation. We are predicting at IDC that by twenty twenty eight, one in four customer service agents will have evolved into the role of concierge to customers, bridging the gap between quota, earning roles, sales and customer service, and pure service.
Now if you look under the surface of this prediction, what it’s really saying is that those customer service reps will not be in that role.
Their their role will have evolved.
They there may be fewer of them, and it’ll be a little bit more specialized because that one and four are the folks who are going to be replaced by agentic AI in call centers and customer service scenarios.
But not all of sales can be automated through agentic AI, and and I wanna make sure everybody heard that. Not all of these roles can be automated in this way, and and it’s really important to recognize the reason why.
There’s a little bit of, a mythology about the distinction between selling in b to b and b to c digital commerce scenarios.
And I’m gonna lump direct to consumer or d to c digital commerce in with that b to c bucket because the the motion looks very similar.
And this mythology is that when you’re dealing directly with the public and selling directly to consumers, that they are more emotional, and you need to appeal to them emotionally to actually make the sale. But but for some reason, that that isn’t the case for b two b. That’s the mythological part of things.
I would argue, and there’s quite a bit of of data to back this up, especially if you look in, like, academic journals and stuff like that, that in b two b, deals are made and broken every day based on whether or not an organization likes the salesperson or enjoys interacting with that company, or they feel like the supplier that they’re talking to has their best interest at heart. The manufacturer, you know, is a good partner to them.
And so you have to recognize that there is an emotional component to sales and b two b as well.
Because you have to build trust to establish those long term commitments, those long term relationships.
And that’s frankly what’s going on on the consumer side of things too. You have to build trust. It’s just the appeal to pathos on the d to c, b to c side is much more direct. Whereas it’s a little bit more subtle, and it’s couched in a lot more practicality and transactional language and language about, you know, revenue outcomes and margins on the b two b side.
But that’s just because they have different focus areas because of the nature of the fact that it’s a business buying from another business. There’s still an emotional element to that. So you cannot fully automate all of the sales process because you will need to give some level of white glove support to the businesses that, are buying from you at high volume in particular, but the ones that you think have promise. To be able to buy in that high volume later in the future, you would want to be able to provide them with that kind of service or some kind of introduction to that service level, right away.
Because in doing that, you help establish that bond, that trust, and that long term relationship. So we predict that one in four, customer agents will be agentified. They’ll be turned into agentic AI, and that the rest will have to become more specialized. They’re going to become concierge to customers.
They’re gonna be delivering that white glove level support that I just mentioned, and they are going to help make customers, whether we’re talking about business buyers or end consumers, feel more comfortable before they commit to the purchase.
AI enabled CPQ for complex business models.
This is a topic where, again, I could really go off into the weeds, but I’m gonna try to keep it really high level. And I’ve got a little bit of data I wanna tell you about, while we’re going through this particular point.
AI driven pricing models enable real time quote optimization based on market indices, inventory, and customer context.
Machine learning, which, again, huge advocate of, love machine learning, enhances guided selling by recommending configurations that align with buyer behavior and sales priorities. And by the way, just to just to hearken back to what I was saying about agentic AI a moment ago, agentic AI is not one algorithm, one type of AI. It is multimodal by default, which means that, whenever we’re talking about agentic AI, it’s usually got a lot of machine learning involved. And in fact, the the core of what’s going on even in, large language model generative AI is still machine learning via a neural net. So, again, don’t discount machine learning just because we’ve got fancy new buzzwords and it’s evolved to become, a more generative technology. Sometimes you don’t need AI to generate novel content or novel code for you or to even provide you with analytics, though all these things are really handy. Sometimes you just need really good machine learning to help guide the sales process.
Advanced automation reduces manual inputs across the quote to cash cycle, driving faster conversion and lower operational cost. And generative AI capabilities, which, again, are on the roadmap for most CPQ vendors, will streamline content creation such as contract text. So if they touch on CLM capabilities, contract life cycle management capabilities, they will be able to do that. They will be able to generate quotes. They may be able to automate things like emails and, you know, base level correspondence. But, again, as the sales cycle progresses, you want to get more human centered.
You really just wanna automate the things that are happening at the front end of a lot of these conversations just to get the ball rolling and to give access to information.
But all of this can be more personalized too by leveraging AI.
And just to kind of give you a little bit more meat to chew on, for the CX path survey, which is a new deliverable that IDC has begun running, I constructed multiple questions specifically for CPQ.
One of them was about optimizing CPQ performance with AI specifically. And the question that I asked to a hundred and ninety professionals who worked every day in CPQ systems. In an ideal world, how would you most like to use AI to improve the performance of your organization’s CPQ system?
The number one answer was improved sales forecast accuracy. So that is a very specific kind of AI that is leveraging predictive analytics, which, again, is taking in a lot of data from inside your organization. It is looking at historical information to try to create, new, avenues of opportunity for the future. It kinda gives you an idea of the the rhyme and reason and what you can expect to come, you know, from seasonal sales performance in the past. Now part of the problem with predictive analytics, though, is that they have to utilize historical data and external signals and then, you know, extract from that information an amalgamation of what the potential outcome may be, but it’s only a potentiality.
It’s no more accurate than a weather forecast.
So if if you are one of those folks that lives in an area where you know that, you know, just wait twenty minutes and the weather will change and that the the weather forecast is hardly ever accurate, that’ll give you a sense of what you can expect from a lot of predictive analytics. It’s very helpful, particularly when you’re planning sales cycles, when you’re planning inventory and things like that. But you cannot forget to have humans on the loop who are capable of not only taking in the signals from these predictive analytics systems, but are also capable of looking beyond them. Looking beyond them for more external signals, and getting a layout of land that is a little bit deeper than how the data is presented. So we love to do things that are data driven, but just know no amount of data will equate to a crystal ball. You still have to be intelligent and strategic about how you are leveraging it and the people, the experts who are leveraging these systems.
The number two answer to how would you most like AI to improve your CPQ was optimizing discounts and promotions, prevent revenue leakage. Really important. Unfortunately, a lot of CPQ engines don’t really touch on promotions or influence what kind of promotions are being offered on the front end of a lot of commerce activities, but that is slowly changing.
The number three answer was automated workflow creation and optimization.
Number four, predicting customer buying patterns. And number five, automating complex pricing calculation. So, again, going back to revenue, revenue, revenue, revenue.
Very, very important.
So the next bit of data I wanna talk to you about is key business concerns that are typically addressed by CPQ solutions. And the question asked was, what are the main business concerns and initiatives your CPQ solutions are meant to address? Well, number one with a bullet. Over a quarter of respondents stated that improving pricing accuracy and consistency was the most important thing that they wanted their CPQ to provide for them.
Followed by number two, enhancing CX, that all important customer experience and the engagement involved.
Number three was improving quote accuracy and compliance, followed by subscription management, and then billing automation. Now those the the number four and the number five, subscription management and billing automation. Not all CPQs do both of those things. So make sure you ask around, if you are specifically looking for those options.
And let’s go ahead and move on to the next topic, which is the big trends. Number one, predictive pricing with historical data and market signals. Now remember the caveats that I gave earlier about using predictive modeling.
It’s very valuable, very valuable to have that data at your fingertips, but make sure you have competent people at the helm that can augment and interpret that data, and actually execute on the actionable insights that are being provided by these systems.
Namely, that AI powered CPQ systems use historical transactional data, as I mentioned, competitive benchmarks, and market indices to generate real time optimized pricing.
Pricing, huge topic right now in this era of ubiquitous uncertainty.
Predictive models adjust for customer segmentation, buying behavior, and inventory levels minimizing margin leakage. Now, again, not all CPQs are created equally. Some do this better than others.
These dynamic engines enable differentiated pricing strategies across regions, channels, and product life cycles, and buyers report reduced discounting volatility and stronger alignment between pricing, policy, and deal execution.
Trend number two, generative AI for quote automation and conversational interfaces. So as much as I was just talking about my love of machine learning and about how a lot of Gen AI leverages robust machine learning under the hood, and agentic AI uses a lot of machine learning because it’s not only using these LLM models, but it’s also using, a little bit of, everything.
You know, it might be leveraging vector technology and machine vision. It may be leveraging, you know, RAG, which is retrieval augmented generation where it is, taking data that you have a moat around, so it’s still protected and proprietary. But it’s taking that, creating kind of a canonical data layer, which again abstracts away some of the more sensitive aspects of this so it doesn’t penetrate the public facing models and then integrates that into the insights that are delivered up or anything that is generated in the content so that it’s a little bit more specific to your brand, your products, your needs.
There’s a lot to unpack here, beyond that, but let’s just say broad strokes. Generative AI accelerates quote generation by producing personalized compliant outputs at scale, eliminating manual bottlenecks.
Embedded chat pots, chat bots and copilots guide sellers and buyers through complex configurations with natural language interfaces. So this this one’s really important to recognize. When I say conversational interfaces, that’s because in many cases, what you’ll see particularly in enterprise apps is that the agent or the agentic AI is a chat interface that is superimposed on on top of the system that you are already using.
So, whether that is, you know, a product that is an ERP, a CRM, a CPQ, it gives your operators the opportunity to just query the system if it’s on the back end to say, I would like to create a workflow that does x, y, and z. And then it’ll model that for you directly in the chat, and then you can say whether or not you want to change this or whether or not you would like to run with it. So that’s an example of how you can use, these sort of agentic or generative, conversational interfaces in a very, very practical use case, in this case, agentic workflow creation and augmentation.
But then on the front end, these can be really powerful tools for customer experience and customer service. Because especially if you are incorporating data from your, order management system of record, this can be a really powerful way to deflect a lot of superfluous tickets coming into your help desk about where is my package?
Is this coming?
And when you’re talking about, you know, b to b processes, it it could be just as easy as, you know, helping aid the negotiation process, helping to, define what some of the products do, which components of various configurable products are most harmonious with other ones.
If your product catalog is tight when it is being fed into the CPQ and your inventory is accurate, you can do a lot with agentic AI. But it’s almost always a just just a in most simplistic terms, an evolved UI user interface, whether that’s on the back end or the front end. And that has, yes, a profound amount of utility, but it’s just another way of doing the same things that you would have been doing through other mechanisms throughout your business.
So when when to adopt them, I think, depends a lot on the cost of the technology, the cost of compute.
And you would want to partner with really strong vendors who have a handle on that and that information who can give you some sense of that before you commit to buying the CPQ.
They also will integrate with CRM and product data to allow for real time quote adjustment during customer engagement, which is really helpful. You know, the goal is not necessarily to deflect, deflect, deflect, but just to augment the capabilities of your, customer success and customer service teams. So that way, even when your call center is closed, for example, you still have some mechanism for your customers around the globe to be able to communicate with you and get the information that they need at their fingertips.
An early adopter site gains in sales velocity, which should make sense given the nature of what these technologies do, content consistency, and, representative productivity across hybrid sales motions. And just to add a little bit of additional data to this, I asked, again, in the CAX path survey, I asked a hundred and ninety CPQ, users to rank the top five processes for their CPQ based on the amount of time that your company uses to perform the process. So what is the most complicated time consuming thing that you as an organization are doing in the CPQ.
And number one, with forty four percent of the respondents stating this, it was managing and updating the pricing and the product catalogs.
Well, this is what AI can help with significantly.
For example, the pricing, ML can help with that all day, every day.
And then when it comes to the product catalog, generative AI is really helpful here. But just like I mentioned with the caveats that I’ve been giving this whole time, you would want someone competent on the loop, not in the loop. They’re not prompting the system necessarily to get a bunch of outcomes, particularly in this in the case of agentic AI. That gets a little bit beyond prompting, hence why we say they’re on the loop, not in it.
If it’s generative AI, they’re in the loop. They’re actively guiding these processes. Your representatives are, your your team members, your talent is guiding these processes with their prompts, and prompt engineering becomes a really big part of that conversation. But when we’re talking about agentic AI, they are on the loop.
They are monitoring the process. They’re not necessarily forcing it along via prompting, and they’re refining this. They’re helping to train that model for you specifically as an organization as they’re working in it. So whether you’re using pure generative AI technology or agentic, which is basically generative AI on steroids, you are still going to reap benefits from that AI being in the mix because it can generate product content that is compliant and brand voice at a massive industrial scale.
It is it is mind blowing. So if your teams are spending a lot of time refining the product catalog, particularly for complex configurable products, then then this may be a godsend for you. The number two most time consuming thing, was generating and customizing sales quotes, which again is something AI can really benefit, can help your company benefit from. And then also implementation.
I mean, there’s no getting around it. Implementing a CPQ is no small task. These are complex pieces of software with complex integrations.
So as much as I would like to say that AI can, you know, wave all the that away with magic wand, AI can help with the process, but it’s it’s still going to be complex. I would recommend, making sure that you have, competent partners in that regard that can help you to make that as smooth of a stand up process as possible.
Alright. So let’s go ahead and move on to the next point here.
And that’s trend three, agentic AI workflows for self optimizing sales operations. Okay. I’ve mentioned this so many times and I’m sure you’ve heard this a lot too throughout the entirety of twenty twenty five agentic agentic Agentic.
Well, Agentic AI really can come in handy. It introduces autonomous decisioning into CPQ workflows, enabling self tuning logic and adaptive business rules. So as I just mentioned a moment ago, this is like generative AI, but on steroids. And the reason why is because it has reasoning capabilities.
You wanna know something that’s a little spooky?
The architects of this AI, so whether we’re talking about OpenAI or Anthropic or whomever the the AI enabler is that has built out the the agentic AI under the hood, they they are not really clear on how it developed these reasoning capabilities.
It just seems to be one of those things where when the model gets big enough and it has enough data points, it just takes on this capability, which, is either really cool and really fascinating and something that you would want to research, which is my point of view on this, or it’s a little scary.
Maybe you’re somewhere in the middle, but but let me try to win you over a little bit because the reasoning capabilities are phenomenal for certain kinds of use cases. And one of the ways that that they tend to excel at the best is literally trying to find a way to be gently persuasive.
So it helps with quoting. It helps with frontline sales. It it can help your operators feel more comfortable leveraging the AI.
But then again, it can also do very practical things because it is taking in a lot of data from a lot of different sources both within your organization, again, with a moat to make sure that that information is kept secure.
But also from the public models, it is then amalgamating all those things using many layers of machine learning and generative AI technology. Again, multimodal by default, it can actually self tune workflows, business rules and logic. And it can actually make your business run like a finely tuned machine without having to run through a lot of layers of middle management to extract that decisioning.
It actually, accelerates the timeline to decisions in a radical way.
These systems learn from user behavior. So whether that’s customers on the front end or your operators, everyone’s training your specific instance of the agent model.
And it can also learn from quote performances.
So that taking that historical data and doing something actionable with it. And external triggers, like news about tariffs, for example, to optimize pricing paths and configuration flows.
Sale self healing capabilities within the agents reduce administrative intervention and improve system resilience under changing business conditions. Again, the operator within your organization is on the loop, not in it. They’re not a bottleneck.
They’re watching the system and making sure everything is running smoothly while helping to train it. And then also strategic value lies in enabling leaner operations over time, leaner teams that can maintain agility and, in fact, maximize agility while scaling across channels, skews, and different geographical territories.
So let’s talk a little bit about the impact of a CPQ system on customer lifetime value and retention, some more juicy data from our CX path survey.
I asked the same cohort of a hundred and ninety different CPQ users. Does your organization’s current CPQ system enhance customer lifetime value and retention? While the overwhelming majority, forty percent, said it was crucial.
It had a crucial role in enhancing CLV, and thirty six percent said it played a supporting role. So the overwhelming majority of respondents, about, a little bit, a little bit under seventy five percent, stated that this was so important to their business that it was crucial that it it supported the sales teams, but that the uplift was noticeable enough that they that they responded in this overwhelming fashion.
So can you imagine how much more effective that would be if you added really intelligent AI under the hood?
So let’s talk about the future of commerce. One of my favorite topics, CPQ’s role in unified revenue management. So using AI enabled CPQ to unlock uncertainty proof sales.
So one of the things that we mentioned at the top of this conversation was enabling subscriptions and how not all CPQs can do it equally.
But this is crucial.
Subscriptions can unlock some really interesting opportunities for revenue on a continuing basis and also complex things like usage based pricing models, sometimes even mixing the two.
So the mess most flexible CPQ platforms for commerce support hybrid pricing models, which includes things like subscriptions and usage based billing and tiered entitlements within a unified quoting interface. So you won’t want multiple CPQs.
And usually organizations that have multiple CPQs or multiple CRMs or multiple ERPs as we’re all aware, it’s via acquisition. You know, they’ve acquired a new, brand extension or a new product line, and they inherited the stack that came with it. But now a lot of organizations are thinking about, well, what’s next? Can we can we unify some of these systems across our portfolio of products and brands?
And ideally, you would want a CPQ that allows you to do this while still supporting very complex sales motions.
They would also be able to support dynamic configuration, to adapt packaging, bundling, and renewals to reflect evolving consumption patterns. Renewals can be very challenging. So, ideally, having a CPQ that acts as a software technological partner in that process can be a real ace in the pocket.
Quote generation would also benefit from this, because you can automatically do things like co terming, proration, and renewals trigger setup to reduce revenue leakage.
A lot of revenue can be dropped between the front of the house and the back of the house because it’s like, you know, the the they’re not talking to each other in the same language.
One’s Mars and one’s Venus. Right? But I think that a CPQ can bridge the gap between the two so that they both speak the same revenue love language.
Enterprises report improved monetization agility as recurring revenue strategies replace traditional one time sales models.
Why spend all of that time and energy developing a relationship with the customer only to have the same order come in over and over again? I mean, that that’s cool, but you can set up a subscription model that offers intelligent recommendations for bundling, intelligent recommendations for service packages as part of that bundle. You can do things with the technology that enhance that process, boost the sales, and boost your revenue outcomes at the same time.
Seamless integration across the revenue life cycle. So modern CPQ can act as an operational core across the quote to cash continuum because it connects CRM, ERP, billing, subscriptions, contract generation systems.
And it can be pre integrated, and it provides many pre integrated connectors to streamline handoffs between sales, finance, fulfillment teams.
Again, that merger, that that beautiful Venn diagram between the front and the back where commerce also helps bring all these things together. CPQ acts as the enabler in this b two b sales motion.
And then you can minimize your manual reconciliation by leveraging a CPQ properly, particularly one with a lot of AI in it. CPQ plays a foundational role in emerging RLM platforms because it reduces silos across front and back office. Think about how many different pieces of software your enterprise may be leveraging for financial operations.
What if your CPQ could do some of that for you while also communicating that data upstream to your sales teams to the extent that it’s helpful for them to know?
Maybe that would generate more actionable insights about how to reduce revenue leakage. And if you’re in a purchasing role, having more integrated systems and a, central source of truth helps you minimize costs.
Businesses gain enhanced forecasting, compliance, and agility through unified data and process orchestration.
So all of these are incredibly potentially beneficial to businesses as, you are using your CPQ to get maximum traction through it.
But then let’s not forget customer experience. What is this like for your customers and how can you leverage their sentiment? Because again, b2b buyers do buy things based on sentiment still, right? Like whether or not they have a certain feeling about whether or not the business they’re trying to do business with is trustworthy or if the salesperson they’re talking to has their best interest at mind.
You you cannot neglect the fact that this is still an important component to things.
CPQ enhances CX by enabling real time self-service configuration, interactive portals, and transparent pricing. So self-service, it unlocks self-service and b two b, particularly if you are merging commerce functionality on the front end with the quoting mechanics that are going on in the CPQ. And if you create an interface where your customers can see this, they can play around with the things that are in their cart just like a consumer would. And this is important. This is really, really important during uncertain times because it shows a certain amount of insight and frankly, business empathy towards your buyers.
If you stage an environment where you know that uncertainty is going to be persistent, What are these tariffs going to be like? What is the future of taxation? Right? Like, these are not sexy topics to consider, but they’re crucial for preventing revenue leakage. And if you provide your buyers an opportunity to play around with the cart, to see what works and what doesn’t, what they can substitute for other things to try to get around some of these tariffs, you know, maybe certain components that you’re selling are sourced in different geographical territories where this isn’t such a big factor.
They they can adjust their business, and it makes them more loyal because you’ve given them the power at their fingertips to find what works best for them with guardrails and rules put in place by you that are being enforced by machine learning and other sorts of algorithms under the hood of the CPQ. You can see how all these things are kinda coming together in this narrative. Right? And those self-service portals give your customers so much power while still giving you control via those guardrail mechanisms.
So it’s sort of peanut butter and chocolate. It’s the best of all possible worlds.
AI generated proposals and simplified approval workflows accelerate decisions and improve buyer confidence. So, again, really enabling that trust.
Consistent experiences across channels, whether digital, partner, or direct are critical for retaining high value accounts. Break out those white gloves. Don’t be afraid to use them.
But also construct that self-service website. What if they wanna use both?
And frankly, they probably will.
Customer centric CPQ design is now viewed as a strategic lever for differentiation of revenue growth because, frankly, not all CPQs can do this.
And this is especially true in highly competitive verticals.
This allows technology buyers to pivot from b to b into direct to consumer and vice versa via the same stack. So if you are in the market looking for a CPQ and you’re a little d to c curious, you’d like to try that approach to see, how how you can actually, generate even more revenue by selling directly to consumer while there are certain things that, you know, come with that package. And one is, you know, making sure that your pricing is fair between you and your distributors, that you are really sensitive to even the appearance of things like vertical channel conflict, and that you are gracefully navigating this in a way where you’re not alienating your distribution chain, but at the same time, you’re still selling direct to consumer. And it’s a more delicate type roadblock than you may realize. Having good software to help navigate those processes is essential.
And I’d like to provide some implementation best practices for you to ensure success through unified purpose and how you’re leveraging your CPQ.
Now I could I could go on and on and on about this, but I think it’s just, let’s get right to the point. One, start with process clarity. Start with strategy.
Know what you want when you are shopping for a CPQ and when you commit to implementing one. You wanna align the CPQ configuration with actual sales motions, avoid replicating outdated workflows or silos. You wanna design your system for maximum scalability, going back to that modularity conversation. Crucial. You wanna choose modular or microservices based platforms. Just know that the latter of those two, the microservices one, tends to be a little bit more technologically challenging.
So make sure that you have competent people on your side in IT roles that can do that or keep it simple. Right? There are plenty of modular and modular monolith, platforms out there that can do many of the same things but don’t have that same level of, implementation, requirement.
And, those platforms can evolve with new channels, SKUs, and pricing models, prioritize governance. So establish clear ownership across sales, finance, and IT to prevent drift in pricing logic and rule maintenance. That’s a problem with both, rev ops and RLM. There isn’t a single owner usually in either of those two sort of processes.
So have have an owner.
Empower business users. Use low code and no code tools, including agentic AI conversational interfaces to delegate rule management to functional teams reducing IT dependency, so reducing those bottlenecks.
Manage change incrementally.
Very important to to do this because it eases the change management and it makes for a more successful transition to the new technology.
Phase deployment by product line geography or channel to limit disruption and capture early wins. And then finally, ensure executive sponsorship.
You need to tie CPQ outcomes to strategic KPIs, speed to quote, margin improvement, and CX consistency to maintain momentum Because right now, it’s a challenging time to get the c suite to commit to new software. But if you tell them specifically with data to back it up, this is why we need this. And these are the opportunities that are going to be unlocked. You will have a better chance of getting their buy in and being able to bring this new technology into your work.
So there we go. Thank you so much for the opportunity to tell you a little bit about my CPQ research.
Awesome, Heather. Thank you so much, and thank you to our to our audience. So many great insights that you’ve peppered kind of throughout.
So, as we, as we go forward, I hope you you got a chance to hear from a leading analyst in the digital commerce space, from Heather on some of the insights and some of the, some of the aspects of CPQ that you should look at as you’re thinking about your journey. The most noble ones that that I think she she highlighted was one is an access around AI, another one’s around omnichannel, and another one’s around implementation.
And putting all those together and choosing a partner that could deliver on that is something that’s really incredibly important to the success of your eventual CPQ journey.
If there’s any things that, we can follow-up on, we’ll be reaching out to to all of you.
But really thank you for the time here today, and, I hope, you had a great session.