Is AI The Real Deal? Using Artificial Intelligence To Optimize Sales With Patrick Moorhead
Artificial intelligence has become a sort of buzzword in the business world today. Many companies claim to use AI, but is their AI the real deal? We talk about this and more in this episode as Chad Burmeister talks to Patrick Moorhead, CMO at Pricefx. Patrick and Chad discuss how AI is changing the way we do business and how it can help optimize marketing and sales practices. Tune in for more industry insights on the future of AI right here.
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Is AI The Real Deal? Using Artificial Intelligence To Optimize Sales With Patrick Moorhead
I'm with Patrick Moorhead from the Czech Republic in Prague. It’s somewhere I've never yet been, but we'll maybe have to take a visit there. We're excited to talk about the topic, is AI real or marketing smoke? Patrick, welcome.
Thanks for having me, Chad. It's great to be here.
I'm excited to talk to you because having talked to probably close to 100 CEOs, founders, heads of data science, and I have my own opinion on this. Obviously, I've started the AI for Sales show a few years ago, and we get more than 4,000 downloads a month now. I think our audience has an opinion about it, but I'm going to leave that for our conversation. Before we get into what you're working on now and where AI plays a role, I like to help our audience get to know you first. You're in Prague now. Where were you raised? What were you passionate about when you were younger? What was your thing?
I was born in Denver, Colorado. I was the child of two academics. My dad was a PhD Scientist at the University of Colorado. My mom was a PhD in Communications at the University of Denver. I grew up in a household where knowledge and learning were premium. Unlike my parents, I was a creative child. I gravitated towards music very early on. I started studying piano when I was in single digits of age all the way through high school. I went to college in Ohio at a liberal arts school called Kenyon. When I got there, music seemed more difficult than I wanted it to be, so I studied Creative Writing. Eventually, I ended up in Fine Arts and graduated with a degree in Drawing and Painting. Then I moved to the East Coast right around the mid-‘90s.
I have always been interested in computers. There were always computers in my house. I had a Commodore Amiga, Apple IIc, Mac. In the early days, I had Atari and Activision. My parents were very kind to us in that way. I was always fascinated with computers, and computer graphics in particular. When the internet started to happen in the ‘90s, I intuitively was drawn to it and knew how to do it. It was very easy for me to learn how to write code. It was very easy for me to figure out how to use Adobe Photoshop in the V1 and V2 of that software.
I found it easy after college to get into the world of web design. That’s what I did early on in the business was as a freelancer, then later in small agencies on the East Coast helping to build out interactive marketing campaigns and then marketing departments in your interactive marketing units. The business grew up around me. I joined what was then the largest digital marketing and advertising agency in the US. In the early 2000s, there was a company called Avenue A. It became Avenue A/Razorfish that became aQuantive, got bought by Microsoft. That was a crash course in big-league digital marketing. That’s also when a bunch of technologies that we take for granted came online like ad retargeting and keyword search.
Our AI is very differentiated, and it has a critical role in how we think the pricing is going to go over the next decade.
Probably, Geocoding, Google Maps, XE Maps?
Yes, I did the very first programs with AT&T around geofencing and geolocated SMS or messaging. Foursquare was new, and I spent time in my career tinkering with the Foursquare API back when it was a public access API. I backdoored into advertising through digital, and I backdoored into digital through being a design-oriented hacker. Eventually, that led me to wind up working in advertising primarily, but I switched gears because I realized in the second decade of the 2000s that the cool stuff was happening in the product and technology space. I had the opportunity to switch gears and join a company in the US called Catalina Marketing, which was one of the first big data ad platforms that existed.
After that, I spent two-plus years at Twitter, which was cool. It was right after the IPO. I got to see a scaled Silicon Valley company go from no revenue to billions and a quarter in revenue from the inside, which was cool. I also learned then that what I was interested in was doing this high-growth growth-phase tech work. Particularly putting all of my marketing experience from traditional advertising and public relations, industry relations, to design and demand generation, digital advertising, all to work all at once in service of taking companies from early-stage revenue success into large scale revenue success.
I got my first shot at that many years ago when I got the opportunity to join an early-stage high-growth company in Chicago called Label Insight. I helped them grow from the $3 million range revenue to north of $15 million, scaled from 50 employees to 200. That company exited to NielsenIQ earlier in 2021. That was the bookend to the first chapter in this phase of my career, which is about bringing full marketing and brand strategy into organizations that have conquered product-market fit and have a growth path towards becoming big and successful, and need that professional industrial grade enterprise marketing and demand capability added into the mix in order to do it.
I joined Pricefx a few years ago to do exactly that for them and we're on that path. We've been doubling revenue year-over-year. In the time I've been here, we've doubled the size of the organization internationally from a headcount perspective. During the pandemic in 2020, we raised $65 million in Series C funding to continue our growth. We believe we're on the path to become a unicorn billion-dollar valuation company in the next five years.
That's exciting. Talk to me about AI. In the name of your company, Pricefx, it makes me, “Got it.” Share with our audience, where does AI fit in your product?
Pricefx is a full suite solution for pricing analytics, price management and price optimization and delivery. What that means from a practical point of view is Pricefx provides a cloud-based platform that is omnivorous from a data point of view. We can ingest and prefer to ingest data from ERP, CRM, from other enterprise backbone systems. Anything that would contribute to or relate to price setting, we will take in, and we can do a wide range of analytics on that to uncover margin leakage or profitability opportunities, or analyze regional differences or portfolio differences in product mix, all this stuff.
The next piece of the suite is price management. That's where analytics rubber hits the road in terms of how you set prices that are aimed at the highest willingness to pay thresholds for customers, how to set prices that are elastic enough to respond to market dynamics, but also maintain margin and profitability, and how do you do that in a centralized and efficient way so that you can respond to the market, but maintain control over your pricing in a global sense, but also at a very granular level.
Then finally operationalizing that through CPQ and other CPQ-like functions, so rebate management, channel management and capabilities that happen post-contract or post-invoice conditions even, which is how we get to that full suite capability. Ingest data, analyze to know better stuff. Put better stuff to work in how you're setting strategy. Deliver that strategy efficiently into the hands of people that can use it, which is salespeople. Getting optimized pricing and deal guidance, and profitability and margin guidance that is delivered in the context of the customer relationship. Historical transaction data is the pathway to driving profitability at scale for business. That's how our software is designed.
Even on a small scale, I think of our company with four or five products. I met with my CFO and we looked through the line items. We’ve got revenue very well bucketed across the five, but the costs aren’t too well aligned to the five. You first have to turn on the lights in the darkroom and align, “What are my costs by SKU? What’s my revenue per SKU?” Then you can start making interesting decisions. There’s a company out there that has 25,000 users in the space that we’re in, and they charge about half the price that we charge. We don’t have 25,000 users. They obviously figured something out that we didn’t.
To answer your question, where does AI fit in? We acquired an AI company in 2020. It’s a leading pricing AI company called Brennus Analytics, which is out of France. We spent the balance of 2020 integrating the Brennus AI technology into our core optimization capability. Now, we offer an AI optimization component to the way that we provide price management and quoting and price-setting or that operationalization capability as well.
We think our AI is unique in a couple of different ways. I think everyone says that, and I want to spend some time talking about smoke versus reality. Our AI is a multi-agent AI. The easy way to understand that is classic AI or machine learning optimization and pricing tends to look at one band of a price waterfall. The price waterfall is this chart. You can make one in Excel if you want to. It’s built around the idea that you add up your costs, including shipping and delivery and all these things. Then you deduct promotions and other factors that impact. Then you wind up being able to determine what’s your actual price and pocket price, what you make and what your margin profitability is.
Classic machine learning optimization tends to focus on one band of that waterfall and say like, “Let’s dig in on these costs of goods or this services line or this logistics and shipping costs, delivery costs, and figure out if we can optimize that piece of the waterfall.” Our AI is designed to take the entire waterfall as one optimization project. The multi-agent aspect of our AI can take multiple different constraints from the contract or from the business. It can take multiple different inputs. Not only the company’s own data but external inputs, whether commodities pricing or other things that might impact.
Set up individual agents around each piece of the waterfall that is solving their own column, but also solving towards the collective goal, which could be improved margin, or stem margin leakage or drive profitability through price increase or maintains customer relationship through price stability. In that way, we think our AI is very differentiated. It has a critical role in how we think the pricing is going to go over the next decade. When I talk about it that way, I feel like you should have the reaction of, “I should be doing that.”
Everyone should be doing this. Everyone should be taking advantage of AI capabilities to look at optimizing from our point of view, the way that they price their products for sale and the way that those products get configured and delivered to the customer. The other way that AI works well in our tool is in line in the quoting process for sellers. In our technology, we can deploy within the CPQ intelligence off of the AI and price-setting function. As individual sellers are configuring an order for a customer, and these are large-scale orders. We work with billion-plus revenue companies. The one order form can be hundreds of thousands, sometimes millions of dollars.
The AI is providing guidance to that seller off of a centralized strategy that says, “Watch your discount on this one,” or more interesting, you could probably charge more for this particular line in the invoice and still not compromise your relationship. The AI can help a seller drive a higher close price with more profitability, more commission without jeopardizing the customer because it’s trying to understand also the customer’s willingness to pay based on historical information. Those are two examples of where AI plays a role in our solution.
It reminds me of FedEx circa 1990-something. We had a competitive advantage against UPS because we had this pricing calculator, where as a rep, you could go in and fill it out. Then within 24 to 48 hours, you get the price back. It went through that calculation by a series of people and approvals. There were about fifteen sign-offs, I think, but it came back quickly. UPS would take 4 to 8 weeks to go in and calculate all that. By having it at your fingertips and now 24 to 48, that's starting to be too long these days. Why do I need all these people involved when the technology can instantaneously do better performed by looking at all of those individual line items? I don't need twelve people to sign off on that.
Everyone should be taking advantage of AI capabilities to optimize how they price their products for sale.
You've highlighted one of the key advantages that we talk about when we sell the technology to our customers, which is we look at quote approval cycle times to reach that right price that everyone's comfortable with that feels like it's in line with margin, profitability, guidance. Finance is signing off on it. We have found on average that we can reduce those cycle times by 25%, 30%, sometimes 50% for customers. Which, in practical terms, for one of our customers meant that their average quote approval cycle time shrunk from 17 days to 5 minutes. If you play that through at scale for a $10 billion organization, they're saving hundreds of days of man-hours on optimizing the way that they deliver their pricing guidance and profitability optimization into the hands of their salespeople.
I’ll tell you what that makes me think about. The value of a differentiated line item in your product stack, a non-commodity. If you’re selling computers and you’ve got some piece of software, that’s an 80% margin and the computer is a 12% margin, then you need to have more of that. As AI comes into quoting processes, all of a sudden, if you’re a somewhat commoditized business, you better start adding on those line items that can command a higher percentage. That way, when you look at the whole of the parts, you can say, “I’m 10% there on your bulk.” Then they’re 12% margin, but on the software side, I’m at 80% and that makes a lot of sense. Think about if you didn’t have AI, could you even be a business or would it require hundreds of people? What would be different if you didn’t have AI?
We sell AI. This is an interesting fork in our conversation. We both use AI in the service of selling what we sell, and what we sell includes AI for the end customer. I’m using AI in my demand and marketing organization to do things like predict market opportunities or understand customer data or customer behavior and buying intent data.
We’re deploying AI in our own sales process of selling our AI to our customers. It’s everywhere. My answer to the question, “Is AI smoke and mirrors? Is it real?” It’s absolutely real. If you’re not figuring out how to use it somewhere in your business, you’re probably behind at this point because it has a role in everywhere you look in an organization.
To answer your question, we would have a very nice business without AI. Centralized price management, efficient price delivery through quoting and after invoice management for those conditions. Pricing analytics to identify margin leakage in revenue and profitability opportunities. That is a stable business. That is a one-direction business. It is only getting more important. It’s only accelerating in terms of how fast you need to respond.
Being a cloud provider of those types of solutions, I think our growth path is clear. However, increasingly it is becoming table stakes in our industry to provide an AI optimization capability to customers who are buying that core functionality. We would be fine without it. We would not be a market leader without it. You need to be delivering AI optimization into pricing to be a market leader.
Then there’s some debate about what equals AI in our industry. One of the ways that we have tackled that is we very much believe in a clear box AI and a configurable AI. Many of the companies in our space do not provide that. We’re differentiated on the multi-agent aspect of it but also, when you buy AI optimization from Pricefx, you get the keys to the machine. You can go and see the way the algorithms are configured. We provide the opportunity for you to tailor those yourself within the software.
You understand how the optimization is working and you can put your hands into manipulating the way the optimization works. That is different from many of our competitors, who we believe offer a black-box optimization in pricing, which says, “We have smart people that build smart stuff. Give us your data and we’ll tell you what to do.” I’m skeptical of how much the people versus the tool is involved in an AI when it’s black-box like that. I can tell you on our side, our AI is legit. It is a real AI capability. We don’t have people behind the scenes that are doing the work. It is a multi-agent system that is delivering the guidance that we’re providing.
I had a talk with someone on AI around media, movies and Netflix. If you think of Netflix black-box, if I watch a movie and it starts serving me up to certain types of content, versus where the industry is heading is, “I’m going to pay you a dollar and watch your show.” Now, it’s a different optimization. There’s so much untrust in the world now. I want to have some level of control and understanding of what’s in that clear box, as you call it. That’s fabulous.
On the sales and marketing side, tell me a little bit more about that. You said you’re using it obviously. Intent data has become a big thing. I’d had my hand in a few intent data projects. One of them was with the Unified Communications company out of the East Coast. They found that the intense signal was too late to where they had already made a decision. What they found is, “We’d rather have a smaller intent signal if you look at the standard deviations. Let’s get it earlier in the buying cycle before it goes too far above the threshold.” What have you found works in the intent data side on the marketing spend?
Little so far, but it’s new. I think the only way you can figure this stuff out is by doing it and by being willing to run tests fast, fail fast, look at a lot of data, pivot quickly, get comfortable with the idea that done is better than perfect. My team is probably sick of hearing me say, “Ship it.” We try to move very fast around that stuff so that we can incrementally get smarter about how we’re using it.
Our approach is we are getting smarter about ingesting intent data from third parties. We also are trying to build a capability where we’re defining what intent is through our own first-party engagement with the market. That’s interesting because it’s not a pure software assignment. You have to develop content, which takes artistry, and it has to resonate with the audience. The way you understand how you’re getting that correct is whether or not the market responds when you publish that stuff. The only way you can say that they responded is if you have the tools and the technology to measure response, and you have that sensor calibrated. It isn’t a pure science effort.
It brings it back full circle. Me as a creative person from birth, I’m working in a world of highly-structured scientific software. There’s still this creative aspect to it for us which is, “Someone is clicking around on a review site, G2 crowd or something spins off a certain level of intent. What if we could get there sooner or get a stronger signal if we use our humanness to understand what is the question that our buyer most wants to ask and wants an answer to? Proactively build content that answers that and put that in front of them. If it works, we’ll see this X and Y thing happened in the machine. This click will happen or this next click will happen, or this conversion will happen.
That’s where we’re trying to link together the artistic side of marketing, that storytelling, that humanness about understanding what the needs are, back into feeding scientific and structured data systems to give us the true pure understanding of, “What is this customer? Why are they interested in us? What do they want to buy from us? Can we do it for them and move it along that way?”
It’s interesting because this personal branding for CEOs and founders on a one-to-many basis is precisely that. My company drip a quote every day from conversations like this. On the next day, we drip a 30 to 60-second video from a clip of one of these conversations. It personalizes me in the market. I’ve got the ScaleX brand, my nonprofit brand, and I’m personalized across all these different brands.
I think about what that means from a CEO to their market compared to what you’re doing, where if you’re a buyer, that doesn’t mean you’re this personality type. It means you have a high propensity to be that, but that might only cover 70%. Now, let’s go for the next 20% and the 10% after that. There are probably 2 or 3 different buyer flavors, and the messaging within those have to be differentiated. That’s how you get to optimization. This is not smoke and mirrors.
There are more practical ways that we deploy as well. For example, one of the challenges in digital marketing is making sure that you have as much complete data in your CRM about a particular prospect organization. We’re using some AI tools. I won’t drop names on your show, but there are tools available in the marketplace that can look and say, “On the website, we’ve seen this IP address before. Then we see that IP address, but it’s over here on some webinar and it’s linked to this guy’s name. Can we look that name up? Yes, we did and we found this email address.”
Closing gaps in the systems is a great use of AI in sales and marketing because the quicker I can get to a complete data profile of a buyer, the better off they are going to be, believe it or not. It sounds creepy, and I think buyers hear that, they’re like, “I don’t want you collecting data on me.” Yes, you do. You want me to collect data on you so that I don’t waste your time bothering you with messages, offers and requests that you don’t care about. The more data I can collect on you, the more quickly you and I are going to be able to determine whether or not you can get value from me. If we find out that you can’t, I have enough data to leave you completely alone.
Salespeople have been traditionally very bad at that activity. I met with the guy who was super brilliant but he missed a lot of the conversation because we’re human. We can only process the number of bits and bites at a time. The AI can go in and go off. I have 100% clear memory. I know your Facebook, Google, LinkedIn. I know all of it. Therefore, “Here’s the upsell based on everything you’ve said in the history of our relationship between vendor and customer. Here it is.” They’re not going to leave money on the table, but for me, as the buyer to your point, I’m getting the best-optimized purchase that I want to make also.
We use AI in the service of selling what we sell and what we sell includes AI for the end customer.
Give me enough data about yourself so that I can understand that in your role at your company, the most important thing I can talk to you about is how we drive success in implementation and how we minimize risk and implementation. If you don't give me the data, then it's possible that I won't know that about you and your company. You might ultimately wind up buying us anyway. We might spend a lot of time talking about the advantages of optimization and price setting.
You're geeked out on that but at the end of the day, for you to get that, we’ve got to cover off this implementation conversation. That's where the data behind the people can help us both get clarity so that we can move quickly. Either towards being successful or going our separate ways so that we can both be successful someplace else.
Last question, think 5 years out, and in tech years, that's like 20 years. Can you even think about where the world is at that point related to AI and sales marketing?
Yeah, I love thinking that way. In five years, I would be surprised if there’s very much of any classic sales methodology, mentality left in the world, particularly in software. This whole idea that deals get done over dinner and I got to fly to my customer, it all about handshakes and relationships. It’s not to say that it’s not, but it has already shifted quite a bit. We’re working off the premise that by the time a prospect engages with a seller, they have already made the decision to buy us. The only reason they’re engaging with the seller is to buy it, to like figure out how to buy it.
The decision about what to buy and us being what to buy has been made well in advance online through self-education. That changes the way that sales are conducted. That trend will further continue. The one-click on my phone and it shows up at my door, the Amazon consumer behavior that we’re all very much familiar with and in love with, is increasingly seeping into the B2B world. There’s an increasing tension around, “I can buy basically anything I want to my front door at home, but when I go to work and I want to buy some portfolio of supply parts for my assembly line,. I’m faxing stuff.” That’s crazy. That’s going to change.
I also think that there will be an increasing willingness to trust and rely on the systems and data to help guide the process. Salespeople probably won’t like hearing me say this, but I increasingly think that AI will, in some ways, come to replace that gut feeling, that instinctive selling, that horse sense, man on the street thing that salespeople have classically hung their hat on like, “Do you know what my value is? I know how it works. I know how it works in the trenches. I can feel a deal. I can feel the customer out.” Data and AI are increasingly beginning to either show that those people are right or wrong. Eventually, the more they’re right, the more the data will win over the horse sense of the salesperson.
That’s a good story there. I went to a website for intent. It’s a data provider that has an intent data product. It’s got a little lock stick signal on the site. I’m like, “I should check it.” It says, “Get price.” Usually, you would expect that would give you the three quadrants with the green checkbox or red and which one you want. It said, “We’ll get back to you.” Literally, I’m on a Zoom and my phone lit up ten robo dials in about four minutes. I’m like, “I just clicked a button and I wanted to understand,” because I use a different company for intent. I wanted to see, is this $5,000?
You were educating yourself.
I’m friends with the CEO and I texted him, “Here are the nine robo dials I had in four minutes. You might want to look into this.” This was a fabulous conversation. Congratulations on your successes and your seed around $65 million with Pricefx, that’s a big deal. If you want to learn more about pricing, I think you have to be $1 billion in revenue. If you have $10, you might not want to give these guys a call, but I bet there’s a cutline somewhere south of a billion. If you’re looking to optimize margins, price, sales, all of it, Pricefx is the leader in this market space. Thank you for being on the show, Patrick Moorhead, CMO of Pricefx. Cheers.
Thank you so much. I enjoyed it.
About Patrick Moorhead
Patrick Moorhead has built a diverse 20-year career at the forefront of digital marketing and advertising, with roles spanning national and global leadership positions at agencies including Razorfish and FCB, and senior strategic marketing, sales and client management responsibilities at Catalina Marketing, Label Insight and Twitter. In his role, he is responsible for all aspects of Brand Marketing and Communications including public relations and social media, demand generation, paid media, content and product marketing, event production and promotional materials.