Impact Pricing Podcast

Matt Knaggs, Senior Business Value Lead at Zilliant, brings a decade of pricing insight shaped by an unexpected leap from industrial safety into commercial excellence. Known for blending analytics, AI, and practical sales enablement, he now helps B2B companies make smarter, more confident pricing decisions by pairing data science with human judgment.

In this episode, Matt and Mark dive straight into the real-world intersection of pricing and AI, where deterministic models still set prices, GenAI fills in missing context, and messy CRM data finally becomes usable.

Matt shares how he built a custom GPT that builds other GPTs, why “pricer in the loop” is essential, and how AI can elevate pricing teams without replacing them. They unpack the future of pricing, the danger of outsourcing expertise, and why curiosity beats perfection in an AI-driven world.

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Why you have to check out today’s podcast:

• Learn how AI can enhance pricing (without setting prices for you) – including specific use cases where GenAI adds context, fills data gaps, and boosts pricer effectiveness.
• Discover the “Pricer in the Loop” model and why Matt believes humans will remain essential for trust, validation, nuance, and internal adoption.
• See how to use AI as a thought partner – to generate buyer problems, value drivers, competitive alternatives, and messaging frameworks that accelerate value-based pricing.

“Don’t hide from all of the advancements in AI. It can be scary and intimidating, but try what you can. AI won’t tattle on you for asking dumb questions.“

– Matt Knaggs

Topics Covered:

03:30 – How Matt Went From Safety to Pricing—and Why the Discipline Hooked Him

04:22 – The Reality of AI in Pricing: What Matt Sees Working (and Failing) Inside Companies

11:58 – Matt Reacts to Mark’s Approach: Using AI to Map Buyer Context

15:34 – When a Pricing Expert Builds AI That Builds AI: Matt’s Custom GPT Story

19:01– The Messy Data Problem Every Pricer Knows… and How Matt Uses AI to Fix It

24:09– Matt’s Honest Take on the Future: Why AI Won’t Replace Pricers Anytime Soon

27:34 – The Threat to Expertise: Matt and Mark Explore What Happens When People Outsource Thinking

31:53 – What AI Can Do for Pricing Strategy (If You Use It Intelligently)

33:15 – Matt’s Final Challenge to Pricers

Key Takeaways:

“AI is probabilistic, not deterministic. You can give it the same inputs and get different outputs. That’s why I’m not ready for GenAI to set prices.” – Matt Knaggs

“You don’t need to learn AI to protect your job. But if you ignore it, the person who learns how to use AI might take your job.” – Matt Knaggs

“The future pricer isn’t replaced—it’s the translator. The one who explains the ‘why’ behind what AI suggests.” – Matt Knaggs

“You can’t outsource judgment. You need the pricer in the loop to validate hallucinations, nuance, and context.” – Matt Knaggs

“AI can scan markets, pull competitor moves, and hand-wave at things you should consider—things deterministic models miss.” – Matt Knaggs

Resources and People Mentioned:

• Zilliant: Pricing optimization & management platform where Matt leads value initiatives
• Stephan Liozu: Pricing author referenced for value-based pricing frameworks
• Salesforce + OpenAI / Claude Connectors: For CRM automation

Connect with Matt Knaggs:

• LinkedIn: https://www.linkedin.com/in/matthewknaggs/

Connect with Mark Stiving:

• LinkedIn: https://www.linkedin.com/in/stiving/
• Email: [email protected]

Full Interview Transcript

(Note: This transcript was created with an AI transcription service. Please forgive any transcription or grammatical errors. We probably sounded better in real life.)

Matt Knaggs
Don’t hide from all of the advancements in AI. It can be really scary. It can be intimidating even to try and keep up with it. But try and learn what you can. Enroll in a practical course. Listen to podcasts. That’s a huge level up for many people on practical applications of AI.

[Intro / Ad]

Mark Stiving
Welcome to Impact Pricing, the podcast where we discuss pricing, value, and the evolving relationship between them. I’m Mark Stiving, and I run bootcamps to help companies get paid more. Our guest today is Matt Knaggs. Here are three things you want to know about Matt before we start:

He is the Senior Business Value Lead at Zilliant, where he helps B2B companies make smarter, data-driven pricing decisions. He’s passionate about combining analytics, AI, and competitive insights for pricing. And he has deep experience in both the science and the strategy of pricing. Welcome, Matt.

Matt Knaggs
Hey, thanks Mark. I appreciate you having me here. I do have to do a slight correction. I’m not the director of pricing — I am Senior Business Value Lead at Zilliant. So I don’t know if we want to go back or just go from here.

Mark Stiving
I just gave you a promotion. Don’t worry about it.

Matt Knaggs
I appreciate that. Thank you. Yep, I’m going to send this to my boss right afterwards. I appreciate that. But everything else you said in there was actually quite true and correct. So no need to revisit that.

Mark Stiving
So how did you get into pricing?

Matt Knaggs
Yeah, you know, as is common within this space, not a direct line at all. If you were to look at my career path, I started off in transportation safety, oddly enough, then graduated to industrial safety, where I became the head of safety for a large metals distribution company.

But going through my MBA program, I got really interested in the commercial side of business. So I started looking for opportunities to get more involved on the front end side of the house, so to speak, and was given the opportunity to join the commercial excellence team at that same metals distribution company I worked for.

There, I took on responsibilities for pricing, go-to-market strategy, sales enablement, CRM administrating — and got really interested in everything that you can do on the sales side of the house to be more successful. And obviously inherent to that is pricing and making sure that pricing makes sense, so to speak.

I wound up in pricing a little over 10 years ago. And it’s become a passion. I study it, I geek out on it, and really enjoy diving into it. I get to talk with customers and companies about pricing topics daily. So I really, really enjoy that.

Mark Stiving
Nice. So what do you love about pricing? Why do you geek out or stay in it?

Matt Knaggs
Yeah, I think I’m one of those people that when you find an interest in something — no matter what it is — you’re going to geek out on it. So we can go as deep in pricing as I can also go on craft beer or running, you know, several other topics.

With pricing specifically, the ability for it to have such an impact on an organization when done right is huge — but it’s often overlooked for a lot of different reasons. I think in some instances, there’s no ownership for it. No one wants to take ownership of it. And in others, people view it as an afterthought or just a number and don’t realize how critical a part it can play in helping you execute on your overarching business strategy.

Pricing and strategy need to go hand in hand. Pricing enables your strategy; it shouldn’t be an afterthought. So helping bring people on board with that concept is something I really enjoy.

Mark Stiving
Nice. I almost want to change the direction of our conversation and talk about craft beer, but we won’t. We’ll stick with pricing.

So as you work with companies and try to help them figure out how they’re going to do pricing better, I assume that you’re seeing a lot of applications for AI today. AI is this amazingly huge, variable thing, and I always feel so bad that I’m using it — but I don’t think I’m using it well enough, or I don’t think I’m using it right, or I don’t even think it’s possible to use it right or well enough.

So let me just toss that out to you: What are you seeing people do, and how is it valuable?

Matt Knaggs
Well, I’ll start with the concept of using AI the right way. Let’s be realistic: what we call AI now, in large part, is gen AI — large language models, starting to be agentic AI. And that’s very young, very new, at least to the broader public.

So for anyone to claim that they’re using it the right way… I mean, even the people creating it are saying, “Yeah, we don’t even know if this is the right way or how it’s actually doing what it’s doing.” Come on — you’re making this stuff and you can’t explain it?

So I want to reframe this concept of “using it the right way” to at least: you’re trying to use it, right? And I hear the ways you talk about using it, Mark — it sounds like as a thought partner, which is a great way to start to get comfortable with AI.

When we think about how a lot of companies are using it, I think there are different levels of adoption. There is the very basic, “Hey, we’ve got to start using AI, so everybody, we now have enterprise licenses for this tool — start using it.”

All the way down to the first companies who don’t just say they’re first, but who say, “Here are the initiatives we’re working on. Here’s what we’re going to do to enable you. This time is specifically reserved for you to begin working on your enablement so it doesn’t just become another task you somehow have to figure out.”

But you know, the irony in a tool that is intended to make us more efficient and effective is sometimes we don’t free up the time to allow ourselves to benefit from the promise of AI.

So is there a right way to use it? We’re all figuring that out right now.

Mark Stiving
Okay, I’ll buy that a hundred percent. I mean… but as I think about pricing and using AI, I have not — I mean, other than you take the big pricing companies and obviously we’re using AI to crunch numbers and look at past historical data, purchase data, etc., and so that absolutely makes sense to me.

But besides that, I’m having a hard time figuring out how people use AI to help them with pricing. Now I can tell you how I do it, but first I want to hear what you do, because I just don’t… I see limited access or limited use for it.

Matt Knaggs
Yeah, yeah. So to talk about this, let’s break down the different types of AI. Right now you’re talking about crunching data and numbers — that’s the algorithmic machine learning, data science approach that’s been around for 30-plus years.

When it comes to using what technically is AI, it just isn’t as attractive, right? Because it’s not the new kid on the block. But to determine what an optimal price is — that would be deterministic, meaning we feed in the same inputs and no matter what, we’re going to get the same outputs every time.

Now, I come across a lot of companies that say, “We want to use gen AI for pricing.” And what I’m hearing in your question, Mark, is a little bit of maybe doubt as to whether we should do that. And that doubt is really justified because gen AI is probabilistic — meaning we can give it the same inputs and get different outputs.

And in the space of pricing, if you don’t have consistency, you don’t have explainability, auditability as to how you arrived at those numbers — there’s a huge risk there.

So what does that mean in terms of a pricer? You’re safe. You don’t need to learn AI—

Actually, no. I wouldn’t suggest that at all.

I would look at what are the things that are not just determining the price setting — what’s surrounding that, that can help inform the price? What are the areas where deterministic AI is actually weaker, where we can supplement what goes into that to get a better outcome?

For example, one of the challenges I’ve heard for quite a long time with deterministic AI is: if you’re just using past transactional data to determine what today’s price is, well… how does the system know about these other market influences that just happened? It doesn’t — depending on how you built your model. It doesn’t. That’s a valid challenge.

With gen AI, you have the potential to start scanning for those types of things. You could schedule tasks — if we’re talking within the context of, let’s say, ChatGPT, which is what most people are familiar with — you could schedule a task to scan for certain changes in the market, moves by your competitor, that would then at least provide a hand wave. Somebody telling you, “Hey, you should consider this before you run with those prices.”

And there’s still that need for — and you hear the term “human in the loop” — I would say the “pricer in the loop,” I’m trying to make that a thing — to actually take that other complementary data and use it to influence what the final price is.

Mark Stiving
Okay, I’ll buy what you just said. I don’t spend a lot of time with deterministic AI. I spend a lot of time with — I’m going to say the words SMB — mostly because they don’t have as much data. They couldn’t really crunch through it as well as large enterprise organizations do.

But one of the things I find fascinating that I use AI for all the time is trying to have it help me think through: How do my buyers make purchase decisions? What are the things that are important to them?

And so, I don’t trust AI — I never do — but what I do know is that it can think up way more things than I can think up. And so I frequently use it to say, “Tell me what problems people are trying to solve when they buy this thing.” And if I ask for 100 problems, it’ll find 100 problems.

And then you start to organize it and categorize it, and it starts to come down to where it’s making sense to me about why it is that people buy something. So I can see that. I mean, we’re still not at “What number should I put on the price?” But we’re certainly at what I call context-driven pricing.

So we’re certainly at understanding better what are those contexts that people are using as they make purchase decisions — so we can then influence or make decisions about our pricing.

Matt Knaggs
Yeah. Yeah. That’s, I think, 100 percent valid. So I’m going to pull on my friend and mentor Stefan Liozu’s work here a little bit to suggest that what you’re talking about — using AI to try and understand the value proposition of the next best alternative — could be a very valid use case.

Now, is it going to be 100 percent accurate? To your point, no. We should still understand that with it being generative, it’s probabilistic.

But here’s the beauty in potentially leveraging it within that context: A lot of companies that are interested in taking a value-based pricing approach say, “It’s not scalable because I have X number of products.” So to determine what the next best alternative is across all of those — and then identify what my optimal price point is based on the differential value there — is way too challenging.

Especially when you get into the context of: “These types of customers buy for this reason; these buy for this reason.” We’re not looking at a one-to-one exercise — one competitor, one product — but we have one-to-many based on the context.

So when you’re looking at scale, it becomes much more feasible to at least get a reasonable approximation of what differential value may be, once you have a solid hypothesis on what the key value differentiator is when you compare your offering versus the next best alternative.

Now, I’m not going to suggest that that means just opening up ChatGPT and typing, “Hey, help me come up with what my differential value is.” You have to actually give the context to the AI tool you’re using. That may mean creating a custom GPT that you train and provide all kinds of context and knowledge documents to, so that it understands the exercise and what it should and should not do in helping you create what this differential value may look like.

Mark Stiving
Yeah, nice. I have to say, since you bring that up, I have started creating custom GPTs for my clients. I upload all of my pricing knowledge — books, blogs, blah blah blah. And then I start uploading content from them and what I learn about them, so that they now have a tool they can use to start asking good questions.

And I built in some key system prompts that make it really easy for them to go find problems or results or things like that. And it’s fascinating. I love doing this. I love watching their response when they see what it puts out. It’s amazing.

Matt Knaggs

Yeah, I’m a frequent custom GPT creator. In fact, I’ve created a “custom GPT creator” GPT. And so it takes OpenAI’s best practices for making the most effective custom GPTs and then forces me to go through that process depending on what the end goal is of the custom GPT I’m creating.

Mark Stiving
Nice, nice. So what else are you seeing, or how else are you using AI to help with pricing? Is there anything specific that you’re seeing that you say, “Wow, this is really good,” because then I want to steal it.

Matt Knaggs
So, I see a lot of different companies who are looking for: “What are all the ways that I can benefit from this related to pricing?” Right? Because again, I will stand on: I don’t want to use it to determine my price. When we’re talking gen AI, I’m not ready to make that leap yet — and I’m happy to dive into all the reasons why not.

But in terms of what we can do, communication is something that is, I think, really often underappreciated when it comes to pricing. Communicating the value that we’re delivering — why what we’re doing is what we’re doing — is something that not everyone is really skilled at.

And so when it comes to renegotiations with customers, I’ve talked to companies where they’re saying, “Hey, we’ve actually built in value conversations into the GPTs we’re creating and rolling out to our sales team, to help them frame up the conversation in a way that will be beneficial, rather than just hard negotiation and ‘Here’s our price.’”

Helping them point out what the value drivers are and what we’re doing to continue to bring them value — in the context of how we’re handling pricing. So that can be around contract negotiation, around price increases, price changes, service changes — all of those things.

I think it can really help upskill and enable sales teams to deliver a much more consistent message, especially if that’s not their strong suit.

Mark Stiving
Yeah, I absolutely see that and use that a lot in terms of: How is it that companies can communicate the value of their products to their customers?

Now, I can say where I’ve never used it — and I love this idea — is: How do I communicate price changes? Or how do I negotiate a new deal?

In fact, I ran into someone at PPS — probably a year ago now — and he said he had built two different AIs to negotiate with each other so he could see what was going on in the negotiations and how to do it. I thought that was brilliant. I haven’t done that myself, but I thought that was brilliant.

Matt Knaggs
Yeah, I mean, the potential for what you can do… we’re just scratching the surface today.

Another interesting thing I’ve seen is this data issue, right? Often companies say, “We can’t really benefit from AI because our data isn’t there yet,” or “We’re not sophisticated enough for that.”

And a perfect example of that would be account-based segmentation. Let’s say the data you have available is based on open text fields, either in your CRM or ERP. People fat-finger things, they call things something else from one situation to the next.

Trying to normalize that data manually could be a really painstaking task. But instead, if you were to upload — say — just accounts, and again we’ll use some type of classification like market segment… and you’ve got maybe 7,000 distinct values in there between typos, nomenclature differences, changes over time…

If you were to tell your gen AI solution: “Take these 7,000 values and consolidate them down to 25, and then reassign them to each of these accounts,” all of a sudden you have something that’s maybe not perfect — especially you, being a fan of context-driven pricing, you might say, “Matt, market segment doesn’t tell you enough context.” Agree, right?

But it may be something where your data becomes much more usable if you’re using gen AI to clean things up a bit to get it to the point where you can make some sense through all the noise.

Mark Stiving
Nice. So Matt, I would never say that, by the way. In my world of context-driven pricing, market segment is the highest level — it is the reason somebody buys a product. And so there aren’t very many market segments. Once you go below that level, there are lots of different subsegments, but at that level there are very few.

Love that example. I thought understanding messy data and trying to make sense of it is huge.

Another use that I’m going to start using it for — and I’m embarrassed to say this — I hate CRMs. I hate them because I don’t want to fill out a form. I hate them because I don’t want to take the time to… and I’m anal enough that it would make me fill out every field and every form.

And I just don’t have the time. I don’t want to do it. But if I could have a CRM that just took my notes and said, “Hey, here are the fields. Let me put it in there for you” — absolutely. And I understand they’re coming out with those.

And so I’m probably going to start using a CRM pretty soon just because I don’t have to fill out forms. So I mean, data cleaning is awesome.

Matt Knaggs
Yeah. And the potential to automate a lot of the mundane tasks — the things that people can’t stand doing. I mean, going to CRM classic use case is: “Hey, I’ve just had an hour-long call with my customer, with my prospect. Now I’ve got to fill out a call report based on everything we discussed.”

And that’s something that salespeople often will delay or postpone just because it’s a pain in the butt, or they don’t have time — they’re back-to-back-to-back. If you’ve got a transcript of the call, or you’re using some type of AI tool during the call as a note taker, the ability for you to just take that and copy-paste directly into your CRM…

Or, as you mentioned Mark, there are even now Salesforce connectors that have been announced for Claude, for OpenAI — and more are coming, I’m sure. It makes it so you don’t even have to worry about doing the work, so to speak. You can focus on the valuable part of your work rather than the mundane.

Mark Stiving
Yeah, and so if we bring this back to pricing for a second… setting a price or putting a price on a product, I find to be probably the least interesting thing about pricing.

The much more interesting thing is: How do we communicate the value of what we do? And when we start using CRMs, when we start using AI, they can help us understand the context of individual buyers much, much better than a salesperson can by themselves.

So I love the idea of using AI to help me in that respect.

Matt Knaggs
Yeah, absolutely. When you think about, “Hey, I’ve got maybe some churn on my own sales team. So I’m talking with a new guy who’s only had two conversations with this potential customer…” And I’ve got ten years of CRM history showing me all kinds of things in terms of what’s been discussed.

You can almost do sentiment analysis — something to try and create a buyer persona based on all the historical information you have. And then there’s even more potential: If you’re looking at how this customer responded to our price changes over time, that can give you very customer-specific price sensitivity, willingness-to-pay signals.

Trying to piece that together before AI… frankly, just wouldn’t have been possible.

Mark Stiving
Yeah, and I think that’s absolutely brilliant, by the way.

And as you were saying that, it also makes sense to me that we could use AI to train a new salesperson on who we are as a company — what are our products, and how do we survive?

And so I think that’s absolutely spot-on.

Okay, so here’s a hard question for you: Where’s pricing going? What are we going to be doing in the future, and how’s AI going to influence what we do?

Matt Knaggs
Yeah… I would say equal parts realist and optimist when it comes to this. Because for the last couple of years now, we’ve heard: “AI is going to take our jobs,” or “It’s not going to be AI — it’s going to be the person who knows how to use AI that takes our jobs.”

And truthfully, with all the developments and the speed at which we’re seeing advancement within this space… one, nobody can predict where this is going. But two, for me to just cover my eyes and say, “Look, I’m going to put my head down and keep doing things the way I’ve always done it,” would be foolish at this point.

I think pricing is going to continue to be a person-centered function where there will be the need for the “pricer in the loop” for a very long time, for so many reasons.

One, I don’t see us at any point in the future getting to where we say, “Oh, this pricing issue happened — well, it’s AI’s fault.” No. That accountability piece is distinctly human.

Now, I’m not going to say that AI will never be to the point where it can’t sniff out certain nuance. That’s one of the arguments of “you can’t get rid of humans.” Unfortunately, as these tools improve, I do think it’s realistic to be able to say that what you and I might call pricing instinct and gut feel — based on what we’ve observed over our careers — is something that can be replicated.

So I don’t want to go in with my blinders on and say, “Yeah, I’m completely safe as a pricer.”

I see it as: learning what you can, beginning to use AI to the extent that you can today, is going to pay off dividends in the long term. Because I believe the future pricing role is going to be someone who knows pricing, but also knows how to use AI.

They’re going to be the translator, so to speak — the “why” behind what AI tells us. Because I don’t see sales saying, “I completely trust AI.”

Even having worked for Zilliant, and having been a customer of pricing optimization companies in the past, I’ve experienced firsthand the salespeople who say: “Your fancy AI doesn’t know my customer better than I do.”

And so there is this trust factor that we can’t outsource to AI either. It’s going to be our pricing professionals who have to be able to explain and validate what we’re getting from AI, to ensure that trust is there within our internal teams.

Mark Stiving
Nice, I like that. So, I think there are only two things that we have as pricing people. And that is — I like to say the words tools and frames.

We have tools we can use, and we have frameworks for how we think about things, or put things in, or make sense of the world.

And the thing about AI and pricing today is that I don’t know two pricing people who use the same set of frameworks. And what that says to me is that AI can’t learn these frameworks because we don’t all agree on the frameworks. It’s really hard to say “This is the way it works.” And so I’m not too worried about that.

I think AI is the tool. And the question is: how do we use that tool to implement the frameworks each one of us believes in, has learned, has used?

So I see that happening. But here’s my biggest fear with AI: I think we’re going to stop having subject matter experts because people rely on AI. And those of us who had to go figure all this stuff out — we’re subject matter experts today. But who’s going to replace us?

Matt Knaggs
Mm-hmm. Yeah. Outsourcing knowledge is really a scary thing. And it’s so easy to do now, too. So I agree with you there, Mark. It’s so easy for anyone to fake what they know.

And it’s pretty easy to parse that out in a live, face-to-face conversation. But where it gets really scary is now, with some of the video creation tools, how convincing they are. Even to where — unless you’re actually live, in-person, face-to-face rather than virtual like you and I are now — even that’s becoming more and more difficult to suss out.

So yeah, I share that concern with you also. And the bigger question I think a lot of people may have is: Will SMEs still be valued? “Why do I need a subject matter expert if AI has, quote-unquote, all the answers?” Which is really scary to think about also.

Mark Stiving

So, when I ask AI questions about pricing — it is never, ever right. Never. Right?

When I ask AI about… I’m just going to make something up… I ask AI about craft beer — I assume everything I read is right, because I don’t know any different. And that’s the problem we’re facing today.

Matt Knaggs
Which is why the pricer in the loop piece I believe is going to be super important. Because as much as AI has advanced, generative AI still hallucinates. It is still probabilistic. And so it’s going to come up with some creative answers that are not valid.

And without an SME to actually validate and say, “No, that’s wrong,” that’s a problem.

Now, where this becomes much more concerning is companies that don’t understand the need for that type of oversight of AI — or the value of that SME to be able to say, “No, hang on a second, that is incorrect.”

Or maybe it’s this blend of: “I’m a pricing SME, but I’m also”—you’ll hear the term—“a context engineer.” We didn’t provide AI with enough context to actually understand what we were asking.

And a very basic thing you can do to get better at that — to naturally incorporate it into how you’re using AI — is: Whatever task it is that you’re setting out to do with your gen AI solution… at the end of prompting it with, “Hey, this is what I’m interested in. This is what we want to do. This is our goal”… now ask: “Ask me the five most important questions to help you understand what our goal is.”

It’s not quick and easy to do it that way, Mark. I don’t see a lot of people doing this. But if you ask, as part of your prompt, for your AI tool to ask you questions so that it better understands what you’re trying to do… the output you get from it improves substantially.

Mark Stiving
Okay Matt, that was worth the price of admission right there. Thank you. Thank you very much for that one.

We are out of time, we’ve got to wrap this up. But last question: What is one piece of pricing advice you’d give our listeners that you think could have a big impact on their business?

Matt Knaggs
Don’t hide from all of the advancements in AI. It can be really scary. It can be intimidating even to try and keep up with it. But try and learn what you can. Enroll in a practical course. Listen to podcasts — that’s a huge level up for many people on practical applications of AI.

And don’t just think about “How might this impact pricing?” Begin playing around with what you can actually do with it, to see for yourself.

And the beautiful thing about AI is that it’s not going to tattle on you for asking dumb questions. So you can try as much as you want and see what happens. And you’ll be the only one who knows if you looked ridiculous trying to do what you did.

Mark Stiving
Nice. Excellent answer, Matt.

And I’m going to tie that to pricing for just a second. I always tell people it is impossible to know how much someone’s willing to pay. So you can never be perfect at your pricing. You will never be perfect at AI. So just go get better. Just go get better.

Matt Knaggs
A hundred percent. A hundred percent agree with that, Mark.

Mark Stiving
Matt, thank you so much for your time today. If anybody wants to contact you, how can they do that?

Matt Knaggs
The best way would be to look me up on LinkedIn. I believe I have my formal name on there as Matthew Knaggs — but that would be the best way to get in touch.

Mark Stiving
And we’ll put the URL in the show notes so people can find it a little easier.

And to our listeners — thank you for your time. If you enjoyed this, would you please leave us a rating and a review?

And if you have any questions or comments about this podcast, or if your company wants to get paid more for the value you deliver, email me: [email protected].

Now, go make an impact.

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