Barrett Thompson is a firm believer that applied advanced math and data science can significantly improve customer lifetime value. For more than 25 years, he has helped Fortune 500 companies improve profitability and grow revenues by delivering science-based, optimized decision models. He has found that relying on the accuracy and objectivity of predictive models to guide daily decisions, rather than more subjective methods, yields superior financial results for B2B companies.
In this episode, Barrett shares AI’s role in pricing, how it enhances pricing strategies, identifies trends, and improves pricing decisions. He also highlights the value of understanding customer-specific needs and using data to create more tailored and effective pricing models.
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Why you have to check out today’s podcast:
- Deep dive into how AI, particularly tools like ChatGPT, can revolutionize pricing strategies, making it more efficient, transparent, and data-driven.
- Gain valuable advice on how B2B companies can simplify and modernize their pricing processes, avoiding the pitfalls of outdated methods when adopting new pricing systems.
- Have a clear understanding of how to effectively implement purpose-built pricing tools to enhance profitability and competitiveness.
“If they undertake an automation of their pricing process into these modern purpose-built pricing tools, they should look very closely and critically at how to simplify their pricing process as they put that onto a superior platform.”
– Barrett Thompson
Topics Covered:
01:14 – Touching a bit on the topic of the Boy Scouts program and how it is a valuable tool for character development and citizenship
01:51 – How he found a path into pricing
03:26 – Discussing the qualitative and not just the quantitative aspect to pricing
05:17 – How automated pricing systems can balance efficiency and personalization
09:14 – Explaining how AI holds great potential in generating “smart prices”
11:36 – Reflecting on the rise of generative AI and how AI in general has already influenced pricing systems
13:50 – Exploring the potential of generative AI to enhance negotiation and communication in B2B sales
15:35 – Discussing how understanding a customer’s specific use case or application can directly influence pricing decisions
16:33 – Talking about how understanding a customer’s specific use case or application can directly influence pricing decisions
17:57 – Highlighting how AI, like ChatGPT, can enhance tools used by pricing professionals by streamlining the process of analyzing pricing data
20:41 – Addressing the “black box” problem in pricing systems and expanding on the idea of using AI to explain its own decisions
24:14 – The importance of deepening the understanding of customer needs, particularly as the push for touchless and self-service channels grows
28:43 – Barrett’s best pricing advice
Key Takeaways:
“An important part of setting up any price guidance, price recommendation or price automation system is to ensure that you’ve identified the factors that were really important in driving price outcomes in the market.” – Barrett Thompson
“Pricing becomes a consequence of having agreed on the value and why it matters.” – Barrett Thompson
“We gather data to enrich the relationship and define places to add more value to the customer. Yes, the seller should be compensated for fair value, but not to be exploitative.” – Barrett Thompson
People/Resources Mentioned:
- ChatGPT: https://chatgpt.com
Connect with Barret Thompson:
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.)
Barrett Thompson
If they undertake an automation of their pricing process into these modern purpose-built pricing tools, they should look very closely and critically at how to simplify their pricing process as they put that onto a superior platform.
[Intro / Ad]
Mark Stiving
Welcome to Impact Pricing, the podcast where we discuss pricing, value, and the inseparable relationship between them. I’m Mark Stiving, and our guest today is Barrett Thompson. Here are three things you want to know about Barrett before we start. He has been with Zilliant for almost 20 years. My wife is the only thing I’ve had for 20 years. He’s currently the GM of Commercial Excellence, where they focus a lot on using AI for their clients. And he is on the executive board of the Flint River Boy Scout Council. Welcome, Barrett.
Barrett Thompson
Mark, thank you for having me on the podcast today.
Mark Stiving
Hey, it’s going to be fun. Before I will jump into content I was an Eagle Scout. I love the Boy Scouts.
Barrett Thompson
Congratulations. It’s a great accomplishment.
Mark Stiving
Thank you. And I can imagine I don’t have kids, but I can imagine if I had kids, I would’ve been just thrilled to be part of the Boy Scouts.
Barrett Thompson
It’s a wonderful program for character development, citizenship, and all the things we want of young people. So congratulations on your journey and thank you for calling. For all the scouters out there, keep plugging away. Definitely worth it.
Mark Stiving
Yeah, I think it’s a great program. So, hey, let’s talk about pricing.
Barrett Thompson
Favorite topic.
Mark Stiving
Yeah, how did you get into pricing?
Barrett Thompson
I come from a math background, actually, I’m a mathematician by training. And as a part of that, in graduate school, I got exposed to the idea of mathematical optimization using formulas and equations. If we all remember, like having some curve in algebra and you’re trying to find the peak of that curve, usually with a zero derivative or something like that. That’s the basic idea. I was really attracted to that. And then when I left school, I began in a company that was focused on mathematical optimization for different kinds of problems, but very quickly found my way over to the pricing side and was just enamored with all of the science and data-driven math behind that.
Mark Stiving
Fascinating. Okay. So, what I find fascinating, by the way, I have a similar background. I started out in mathematics, but I switched to electrical engineering because I wanted to get a job after I graduated.
Barrett Thompson
Well, wait a minute. I actually was exactly the opposite. I went in as a double E. I found that once I got to AC circuit theory, it was like totally crushing me and so I switched over. I became a mathematician. So isn’t that interesting? But the monetization of a math degree is definitely the first puzzle you have to solve.
Mark Stiving
Yes, exactly. But now that I’m many, many years beyond that timeframe, what I find fascinating about pricing is that we go into it thinking it’s so quantitative and it’s not right. I mean, there’s a lot of quantitative stuff going on. Sure. But there’s so much qualitative stuff that we never even dreamed of. Did you have that same aha moment?
Barrett Thompson
Yeah, it’s maybe it’s kind of gradually warmed up on me throughout the years. You mentioned that I’d been resilient for 20 years. That’s correct. A lot has changed in 20 years. I mean, way, way back at my early times we were really focusing mostly on the quantitative aspects. how do we extract some pricing signals from the data? How do we use that to drive a particular revenue or profit outcome? And those are certainly valid, but the kinds of conversations we’re having with customers these days, they’re asking, they see the insights. Say, Hey, I’m trying to stand up to let’s say an e-commerce channel because my market is demanding it. And in that channel, they want the same price flexibility that they’ve enjoyed When they call their personal sales rep. Can I give that to them? The answer is, well, sure you can. Yes, we have ways to support real time negotiation and auto negotiation in an e-commerce channel. But the point there is about customer experience, giving, facilitating a frictionless experience that doesn’t lose any of the qualities that they enjoyed in the human interaction. And doing that as a way to endear the customer to you. And words like endear and frictionless don’t show up in the mathematicians lexicon. So yeah, there’s certainly quantitative, qualitative aspects to it.
Mark Stiving
And I’m absolutely curious now that you said this, if I am, by the way, I was in pricing at National Semiconductor where we tried to do auto negotiations or auto quoting and we would intentionally put delays in so they didn’t know we were auto quoting. I just thought that was kind of fascinating. But I would guess that auto quoting takes the human touch out of it. How do you not lose that?
Barrett Thompson
There might be a couple of ways to look at it. I think the benefit of having some automation behind your price quoting is you relieve the burdensome task of doing sort of analytics, digging through, what did I quote them last time? How have I quoted other customers? What did they spend with me last year? What could they grow into if I were to take this deal off the street at this price? Some of those hardcore analysis. And for that matter, if you go and study four different sources of information trying to gain insight, what will you do if they each point to a different direction about what you should do on price? If you go look at their spend level and you say, well, this suggests I shouldn’t give very much of a discount because they’re not spending a lot with me right now.
But you go look at their application, their end use case, their vertical industry, something else maybe suggest you should give a larger discount because those are normal for that sort of industry. That’s the value your product has. So those are kind of hard to sort out. An important part of setting up any price guidance, price recommendation or price automation system is to ensure that you’ve identified the factors that were really important in driving price outcomes in the market. You’ve quantified which things matter, and then you’ve applied those smartly inside the math and Mark, I think this is what seasoned, experienced sellers are doing with their customers anyway. They’re sensing which factors matter in this relationship. What do they value most in my offering? How should I respect those and treat those in my sort of internal personal discounting logic? And so I think we can put a kind of tailoring into the pricing that feels personal, but it doesn’t require a person to operate it or oversee it.
Mark Stiving
And you still deliver it via the person or you deliver it electronically.
Barrett Thompson
I’d say both. Like think about all of the above strategies, you may for many of the B2Bs we serve, I’d say most of them, they have multiple ways that customers can order. They might be able to call customer support on the telephone and say, I need you to send over a certain product. And that customer support person taking that order needs to have price guidance on how to treat this customer appropriately. It could be that the account rep who’s visiting them every month is going by and talking to them about the book of business and so on. And while they’re there that that customer says, I’d like to put something new into the agreement that we have. You’re supplying me with these 20, 30 SKUs. Here’s a couple of more that I’m buying. I’d like to say, what kind of price could you offer me for those?’
So we need to have an informed price for that situation. And then finally, when that procurement manager or someone is up at 1100 PM on their phone at home, placing an order to get something that they need, they’re doing that on e-commerce after hours. And they could not reach that inside sales person or that outside seller. We want to have the pricing available there too. So we deliver it through all of those channels. And we find that you could do that in a way that those channels are coordinated. You’re not creating any weird experiences where I get my standard discount if I talk to my rep, but if I go online, I see a silly list price that no one pays. Right? So, I’m not going to push the submit button on that shopping cart when I see this ridiculous list price up here. I want to get my discount. So we bring that all together, they should see their discount or they should see discounted pricing that’s similar to what the rep would give them if they were going to wait until the next day and call it in on the phone.
Mark Stiving
Yeah. And so I guess that makes sense if I’m going to try to do a sales person list transaction, right? Because we got to be able to get the order without having a salesperson talk to them. Now, everything you just said says to me, I could do all that with AI.
Barrett Thompson
The generation of those smart prices you’re talking about?
Mark Stiving
Yes.
Barrett Thompson
I mean that’s certainly a huge opportunity in front of us. And if you think about what we’re able to… the promise of AI is to ingest large amounts of data, maybe even very disparate types of data compared to the structured data we might have sitting in our ERP systems. Use that to understand what kind of price I would be giving to this customer. If I were to survey my top 100 sellers in the business, let’s say I have 500 field sellers, think about a customer, think about a product. If you could survey the most successful 100 sellers in the business and ask them for their advice on what they would give to this customer for this product, I imagine you could squeeze a consensus answer out of that. AI is giving us that kind of consensus answer and they’re able to do it for every customer, every product in a way that you couldn’t imitate even if you were to set up a committee of the top hundred or phone a friend for what you would do in my situation.
So yeah, there’s a lot of promise there. I would even characterize what’s been happening over the last 15 years with the use of big data and algorithms to drive prices even before all the hotness and buzz wordiness of AI came along. And there may be new techniques that AI is bringing to the table for that. But even the techniques that were popular and I think are still the majority in use over the last eight to 10 years, they’re very powerful at pulling information out of the data and using it to get those recommendations that we could characterize them as pricing intelligence. Someone might quibble whether it’s artificial intelligence in some very technical way, but it’s certainly data-driven pricing intelligence. And I think that’s what B2B is after first and foremost. And then the technology to do that will continue to improve.
Mark Stiving
So, do me a favor and give me a brief history lesson. I want the history of the world up until a year ago and then the history of the world for the last year, right? So about a year ago we all got introduced to chatGPT generative AI and it’s like, ‘Oh wow, we now know what this does and it’s really incredible.’ Is Zilliant and the pricing system world, did anything change at the introduction of generative AI or the popularization of generative AI?
Barrett Thompson
When I first understood what generative AI was doing, of course it’s fascinating because it’s taking what we might consider to be unstructured data, the large language models, rich amounts of text and context, and it’s able to parse through those and have interactive discussions, if you will. You could ask it questions and get answers. You can feed it prompts and get opinions and perspectives. There’s a part of me that feels like some parts of that are not directly applicable to the pricing domain. That might sound a little heretical. What do you mean it’s AI, all AI matters. Well, I’m not sure how machine vision… I’m not sure how self-driving cars, which are also AI of a type… I’m not sure that those have a direct application over the pricing area, but ways that AI. So let me say that coming up before a year ago, there were a lot of things being done that put intelligence into pricing that might not have worn the AI label, but maybe today they would.
And they still make a ton of sense with the chatGPT going forward. A couple of things I see that might happen there. One is it may be able to give us a better way to have a human-like interaction. Let’s go back to e-commerce and auto negotiation. How does that actually occur? Who goes first? Does the e-commerce system offer you a price for the basket of goods? And then if you don’t like it, if you want a different number, how do you ask for that. As the human, how do you make your case say, ‘Well I’ll order this three times again this month if you send me today.’ There might be new opportunities for how the buyer communicates how they feel about the price back to the chatGPT. I need you to sharpen the pencil. Right? Classic, something like that. Maybe the chatGPT can understand what that means and figure out how to respond to it.
Mark Stiving
So, we teach our AI bot to negotiate better, right? And to negotiate with all the same skills of a salesperson or a purchasing procurement person would. And we could even teach it behavioral economics tricks on how to present prices to make them look the most attractive. Pretty fascinating just in that whole interaction piece
Barrett Thompson
It’s interesting you mentioned the presentation. I haven’t thought so much about that but there is clearly a connection when you think about the three components that need to be present really in every B2B selling situation, your offering needs to have value contained within it. Something about your product, how you service it, how you sell it. There needs to be value there. Value must exist, you have to create it. Then you have to communicate it through the marketing, through the selling process. And then finally, when it comes to the transaction, we want to use price to collect that value. So your point about positioning the price made me think also about communicating the value. And imagine the chatGPT being able to recommend products to you. You could tell it about your application. I’m trying to do a heavy duty submersible pump in an oil field. And the chat GPT can say, well these are the products that would be appropriate for you and here’s why. Can I ask what your expected temperature rating is? Right? And you give it an answer and it comes back and recommends a product. Then when it comes to the pricing side of things, there might be a similar conversation. What’s the best price I could do on this? Well, if your quantity is below 10, this would be my price per unit. If it’s above something, something different. Customer says, I’m looking for a different price, chatGPT might say, could we change the net payment terms on this order? Who knows other degrees of…
Mark Stiving
Barrett, you have no idea how much I loved what you just said because imagine that you said to me, here’s the use case I’m using it for and I happen to know that that use case is really, really valuable and other use cases aren’t so valuable. I could easily charge higher prices because I now know your use case, which is really awesome.
Barrett Thompson
Yes. Or conversely, what if I have a customer who’s buying or wants to buy a product, but this product is a fit for them, but it has a feature that really doesn’t play in their world. The feature realistically adds no value. Maybe that’s a case when I want to discount the product.
Mark Stiving
Exactly
Barrett Thompson
Right. So, knowing something about the end use application, knowing something about the competitive alternatives, the next best alternatives and understanding how you stack up against them, I think those can also feed into that value discussion that you have with the customer. And then pricing becomes a consequence of having agreed on the value and why it matters in that context.
Mark Stiving
Okay. I love this because this is the first time I’ve actually thought about using AI to figure out what the value is for an individual buyer. And now we’re crafting that value model for a buyer and we could craft a price for a buyer. That’s really, really interesting.
Barrett Thompson
Yeah, I think I haven’t seen this tried, but I don’t see any reason, I haven’t seen it tried by AI. I think intuitive sellers are doing this kind of thing anyway. They understand what the customer’s doing at the product. They understand how what they’re selling fits into a larger context. Even subtle things like if my customer is doing a just in time manufacturing run, then my ability to deliver those parts just in time, not a day late, it is really impactful. That’s not even about the product, if you will. It goes back to my offering and I know that they really rely upon my very accurate and timely shipments that are coming every two weeks to keep the line going. So that matters. So when some other upstart on another continent claims to offer the same product at a cutthroat price, but they’re not able to meet that service standard or there’s a risk component implied in that. I think intuitive sellers know their industry. They know that they know they can hold their price up because of that value. And it wouldn’t be a stretch, I don’t think, maybe to train some AI technology to understand where and when and there is value and then how to shape the price according to the value.
Mark Stiving
Yeah. Okay. I interrupted you when you were heading onto the next next benefit. We’re going to get out of chatGPT in our pricing systems.
Barrett Thompson
I think one area that we’re likely to see a benefit is in how chatGPT enables the tools that the professional pricers themselves are using, right? The people who are turning the knobs and dials. I believe it’s quite likely that we can improve the experience for those individuals. An example I’ll give is I think many people in pricing rightly ask, how can I create reports and analytics that are examining price performance to date, showing me trends that are important to me, maybe identifying anomalies and alerting me to things I need to look into. And they do that by creating reports, some of which are standard. They run every day, maybe doing ad hoc report analysis. But if you think of the number of product categories that a typical B2B company has, the number of customer segments they have, you start multiplying these together.
There’s just far too many combinations to go and create and examine the graph for everyone. But you know what, an AI technology could look through 10,000 combinations of how this product category is doing in this customer segment? And is there anything anomalous? And by the way, what does an anomaly even look like, right? And so determine what is anomalous and then elevate what is anomalous so that when the user comes in in the morning, instead of them having to say, ‘I know what I’m looking for and I’m going to go examine it,’ or ‘I’m going to go look at all of the surface area and see what pans out or what pops out to me.’ The AI has already combed all through that in 300 milliseconds and come back and said, ‘Out of all the things you could be looking at today, here’s the top three that I think you’d want to pay attention to.’ For some reason, our hydraulic actuators are showing an unexplained profitability dip in aerospace. Why is that? And by the way, there might be two causal factors. I went ahead and did the research for you. Thank you very much. because I’m a chatGPT. I went ahead and dug in to see, is it because our unit volume is down? Is it because our supplier costs went up? Is there something about the cost to serve change? I’ve already done the second level analysis and I’m suggesting to you that these might be the reasons why we’ve had that profitability dip.
Mark Stiving
Yeah, that’s awesome. One of the problems that I always had with AI in the past and tell me if, if we’re getting over this, or let me just say pricing systems. I say to you, here’s my customer, how much should I quote them for this product? And you give me a number and I have no idea where that number came from, right? Why is that number not higher, not lower? And so let’s call that the black box problem of pricing. And so is AI going to help us solve that problem?
Barrett Thompson
It’s unclear to me. I certainly see the challenge. A part of me imagines also that some of the trendiest buzzwords may actually be more at risk for this problem. For example, neural nets as a way to comb through a data set and understand something like make a price recommendation feels to me like neural nets can be very opaque. There’s a set of weights that are being assigned internal to the algorithm, and we may not be able to see or understand where, what it is that the algorithm is valuing and why it may say with confidence in this situation when you have a customer in this industry buying this much quantity, et cetera, et cetera, in this part of the world, I’m highly confident that this is the right price, but perhaps we don’t know why. With some of the traditional approaches that I’ve seen that say not neural nets, but using other kinds of price segmentation, other kinds of optimization approaches, we can see what’s going on inside of those various reports and readouts that will tell us this factor the geography factor is not playing much of an influence for this particular product. However, the end use application is very strongly correlated to what we’re doing on price here. So I think there’s actually a risk that some of the transparency that we would want as pricers is diminished if we just smear the AI on kind of naively. What’s sort of interesting is whether you could ask something like one type of AI technology, like a chat GPT to explain why a different type of AI technology like a neural net came up with the price it did. That may be a little bit of wishful thinking there, but maybe we can use the tool to explain itself as a second pass, maybe might not come out native in the first pass.
Mark Stiving
So, I was I’m not sure how relevant this is, but I was sitting on an airplane next to a guy who took hundreds of thousands of pictures of fruit on a table so that they could train Amazon’s AI robots to pick and place fruit, right? And so it’s like we’re going to automatically generate data so that we can train AI. And so it’s almost exactly like what you just said. We could say, ‘Hey, go generate thousands and thousands of price points and now tell me why we did this.’
Barrett Thompson
Yeah. I think pricing is not the only, in fact, I think far from the only space where there will be an insatiable need to understand why a certain result was delivered from the AI, not just that a certain result was delivered from the AI. So I hope this will be a problem sort of solved in a large way by the AI industry and that they come back with and it might be a branded something, right? We’ve got chatGPT now. Maybe there’s some other branded thing that explains what it is and why it is that AI algorithms are coming up with some other kind of answer.
Mark Stiving
Yeah. Nice. Let’s go back. I think my favorite thing we’ve talked about so far is the whole concept of value and understanding what an individual buyer’s value is as we set prices, as we communicate value. What else can we do in that realm? Either with Zilliant or just in the future someday when chatGPT when generative AI becomes even better.
Barrett Thompson
Yeah. Some thoughts I have there is that we, even though there’s a great push for touchless channels or self-service channels, yet at the same time our understanding of the customer needs to increase, right? We need to get more intimate with what they’re doing. Like back to use case or application and use application there. I think there are a couple of ways. One is through whatever data is being collected by the commercial systems anyway, right? The customer is buying behavior. Let me backup. The marketing department routinely gathers demographic data on the customer. What is their industry? What is their geography? Maybe something else about them. What we can glean from ERP for example is the customer’s buying behavior. When do they buy? What order quantity do they buy? What mix of products do they buy? And yes, if we push out into something like our quoting tools, the CPQ tools, we can see maybe when we win and when we lose with that customer, if we’re collecting wins and losses, and of course price is one of the levers there.
Availability might be another. I quoted someone something, but I didn’t have the product right away. I had to put it on back order. They needed it urgently. So they had to say no, right? We wouldn’t want to mistake that as declined because of price when maybe it was declined because of order. So I see this need or an ability actually to gather more buying behavior data about the customer through the systems that are surrounding the customer and surrounding the business operations already. Over on the intangible side, like things that a rep knows with their customer every day. It’s a little harder for me to see how we…that, but I don’t want to lose that. I don’t want to lose the fact that the customer says to the seller, ‘Hey, I trust you guys because when I need a new item or I have a new situation and you bring one of your engineers on the line and they help us talk about what’s the right product for the situation that I’m facing. If it’s some kind of technical buy, right? That’s really invaluable to me.’ I don’t know how we go about enriching our understanding of when and to what degree that’s valuable to a customer, but I think it’s important.
Mark Stiving
So in my head I’m imagining I’m wearing Google glass or something like that, if that even exists anymore. But I can imagine if we all had that and AI is constantly translating what’s going on, I think they would do a good job at saying, ‘Oh, that was an engineer that helped a customer solve a problem.’ And now we’ve got another data point in our AI that hopefully that’s many, many years down the road. But I can imagine that happening.
Barrett Thompson
Yeah, I think and something at least it’s my hope, it’s how I’m oriented is that we want to use that information, understand value, not in an exploitative way, but for example, if we realize that this customer values the engineering guidance in selecting products, what are other things we can offer to that customer to enrich the relationship? It might come back when we recommend this product. You’ve put this product into use, you’ve had it for a month, or you’ve been using it in your OEM equipment at the prototypes you’re building. How’s that going? What did you learn about that? Are there other pieces or components that you need? If we find that the customer orders in certain ways from us, like they always order, really, they order a great number of small orders from us every month. And that’s usually the most expensive way for the seller to deliver. Why are they doing that? You could just say, I’m going to charge them more because each order is small and I have a cost to serve. You could do that. That’s not wrong. But to dig in a little further and say, why is it that you’re doing that versus two large orders a month? And you might learn something about their process and be able to recommend a way to help that. So we gather that data to enrich the relationship and define places to add more value to the customer. Yes, the seller should be compensated for fair value, but not to be exploitative.
Mark Stiving
Understood completely. I was trying to figure out how AI could do that for us as opposed to, I mean, everything you said we expect our salespeople to do today, right? So, hopefully that’s happening. Barrett, we are going to have to wrap this up, but I’m going to ask you the final question. What is one piece of pricing advice you would give our listeners that you think could have a big impact on their business?
Barrett Thompson
If they undertake an automation of their pricing process into these modern purpose-built pricing tools, they should look very closely and critically at how to simplify their pricing process as they put that onto a superior platform. Because almost every B2B company that I’ve worked with, they’ve really accumulated a set of arcane practices over decades. And if they, and those in many cases have grown like kudzu and overtaken the world, and they really need to look at those hard and decide what kind of pricing they want to have and manage in their business and carry that over into their tool. But don’t lift and shift a 30-year-old unquestioned practice into a new technology that’s not going to further their ends.
Mark Stiving
And to pile onto that conversation, I, at one point in my life, we actually implemented a new pricing system and it was ridiculous how we tried to get the new system to do everything we used to do. I said that just makes no sense guys. Let’s rethink this.
Barrett Thompson
Yeah, definitely want to use that as an opportunity to do house cleaning on what kind of prices you’re offering to whom and why, and what things you want a price to respond to and why. And watch out for those things that started as small exceptions, 20 years ago only 2% of the orders ever had this and now 70% of the revenue is going through this process. You’re like, ‘Whoa, is that what you want? Or is that just what you’re stuck with?’ Let’s rethink that.
Mark Stiving
Yep, exactly.
Barrett Thompson
That’s my advice.
Mark Stiving
That’s great advice. Barrett, thank you so much for your time today. If anybody wants to contact you, how can they do that?
Barrett Thompson
Best way to reach me at all times is look me up on LinkedIn and my contact information will be current on LinkedIn.
Mark Stiving
Alright, then we’ll have the URL in the show notes 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 the podcast or pricing in general, feel free to email me, [email protected]. Now, go make an impact!
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