Pete Eppele:That elasticity being able to understand what's the difference between a volume change that's more market-driven and a phenomenon of the market versus something that's related to price is really core to that elasticity calculation and unlocks tremendous amount of value in terms of helping you set the right price.
Lindsay Duran: Welcome to B2B Reimagined. My name is Lindsay Duran, [00:01:00] and I'll be your host for this episode. I'm joined today by Pete Eppele, senior vice president of products and science at Zilliant.
Pete, thanks so much for being here today.
Pete Eppele: Great to be here, Lindsay. Thank you.
Lindsay Duran: Before we get started, why don't you tell us a bit about your background?
Pete Eppele: Sure. My role is product management and data science here at Zilliant. I've been with the company now for about 20 years, which obviously in the tech industry is a little bit unusual. Really I have come to Zilliant with a passion for helping companies use data to improve their commercial performance and the decisions that they make around pricing and selling.
Lindsay Duran: Thanks so much, Pete. So our topic for today's episode is about next generation price optimization. But before we do a deep dive on what next generation means, let's talk a little bit about Zilliant current price optimization solution, its history and how it's evolved over time.
So, Price IQ, which is the name of the solution has been a very unique [00:02:00] offering in the market now for many years, tell me why that is.
Pete Eppele: Yeah, Price IQ, we introduced to the market near 20 years ago. If you think about what's going on in that timeframe you had eCommerce was becoming a thing. It was very big. Retail was taking a more sophisticated approach in many respects in terms of how they did optimization. There had been work as well in the hospitality and the airline sectors, but business to business itself, traditional B2B industries, where there were sales reps involved, there's negotiation, really hadn't necessarily had an opportunity or a way to be able to get into the price optimization space the way that others did. And a lot of that comes down to the fact that the business is conducted very differently. When you have a sales rep involved in a pricing decision and delivering the way the price is delivered, the way the price is negotiated, everything is different.
The products are very often different. There can be more sophisticated, they can be configured in cases and all that sort of thing. And so what we [00:03:00] really set out to do was build an optimization system that address those unique challenges of B2B.
Lindsay Duran: So price optimization is a term that's used pretty broadly to mean all sorts of different things. We have a fairly strict definition of what optimization means here at Zilliant and for our listeners. Pete, could you go ahead and define what price optimization means in kind of the strict mathematical sense.
Pete Eppele: Yeah. When we talk about optimization, mathematically, what we're talking about really is a combination of mathematical techniques that come together to determine a best price. And so there are pieces of it that are predictive capabilities, and specifically we talk about price elasticity in the B2B sense with those predictive capabilities, the ability to be able to look at how the customer's market has responded to price in the past and use that information to be able to predict how [00:04:00] they'll respond to price changes going forward.
That's a key component of optimization. And then the other piece of it too, is a true mathematical optimization that allows you to be able to set pricing, not as a rule, for instance, cost plus 30 or cost plus 20, but rather set in the context of an objective of business outcome that you're trying to achieve.
That might be for example, that you might say I'm looking for profit growth in part of my business, or I'm looking for revenue growth in another part of my business, being able to set that objective and use a mathematical program, a true optimization program, to be able to determine those prices.
It gives you very unique capabilities. It gives you the ability to manage multiple trade-offs at once so that you can do things like align pricing across good, better, best products, align pricing across different customer sizes. Even if maybe historically your pricing hasn't been that [00:05:00] rational as it's been negotiated.
Lindsay Duran: Great. Thanks, Pete. So what you're describing there is really a goal seeking proposition is what we mean by optimization; that you are optimizing with a specific goal in mind, not just necessarily saying I'd like price to move in this way.
Pete Eppele: That's correct. A goal based on a prediction of how those price changes will be consumed by the market in the future. So that predictive component together with goal seeking for us really defines what price optimization is.
Lindsay Duran: So price optimization has been around for longer than we've been around. And it really kind of got its start back in the travel and hospitality industry. And we see it happening in retail and other B2C consumer markets. Why is doing optimization in the B2B context so much more difficult than in B2C or travel or hospitality markets?
Pete Eppele: That's a great question. [00:06:00] The core difference really is the presence of the salesperson and the presence of a negotiation process. And that comes in and certainly there's more deal complexity. For consumer purchases, whether it be through an airline or whether it be through a region, there's a very well-known price. It's usually transparent; in B2B it's not nearly that simple. And so when you think about pricing and optimizing pricing, there's really those multiple factors that you're thinking about in terms of how do I present something to a sales person that's going to then parlay into an in-market price that is optimizing my business performance.
Lindsay Duran: There's a lot of AI out there that claims to do optimization and, we've been, I think, fairly selective about the data science techniques that we employ to solve these specific challenges in B2B. Can you talk a little bit about some of the common types of algorithms that people try to apply in a retail environment [00:07:00] or a B2C environment and how those are different in terms of what kind of data you might need, how much data you might need, the conditions under which those work. Compare and contrast the difference for our listeners.
Pete Eppele: Yeah. A lot of the work that's been done on the retail side is very data hungry and it likes to essentially kind of assume that price will be fixed and you can look at a very clean price, quantity relationships. Because every time you offer a given product in the market, you're offering it at the same price.
Those kinds of techniques, what we found through time is they break down very quickly in the B2B sense and the B2B world, because you're not necessarily offering the same price to everybody, or offering it all the time. Again, because of that sales dynamic that we talked about before. So traditional techniques that make that assumption can actually deliver price recommendations that are very far off because ultimately if you have a bad read on elasticity, the market or how the market will respond to changes [00:08:00] that can lead to swings that are just not rational, if you will, within the context of a business, nobody's in a position where they could be moving their prices, 15, 20, 25%, for instance which is often the outcome of some of those techniques that aren't really well calibrated from the type of data that you find in B2B.
Lindsay Duran: So one of the trends that we're seeing quite a bit in B2B now, and to, I guess, compare and contrast this to several years ago is that B2B companies are trying to be much more dynamic in the way that they set and change price. Long gone are the days where you changed your price list once or twice a year.
And that was the end of the activity. How has the increase in what we often refer to as pricing triggers, reasons why you might need to make a price change in your business. How has the increase in those pricing triggers in terms of the frequency and overall just volatility of the [00:09:00] market changed how we approach delivering a price optimization solution for customers?
Pete Eppele: When I look at the root of that, the triggers that we're seeing now kind of moving faster, a lot of that comes from the idea that a digital transformation is certainly kind of made its way into B2B. And so B2B is now selling online which, most have at least a percentage of their sales online.
For instance, something that's new. Their cost, equation, commodities, and all that kind of thing are moving. They have transparency to competition. There's a world of data that's out there and available for companies. And what we see is a real sort of desire and curiosity, build to understand how to factor that information into pricing as you go forward. And so ultimately what we see as companies become more mature in terms of their price optimization approaches, they're using all the available information to triangulate price and understanding the market. And that, [00:10:00] for example, there might be new cues or new information that's available to you about a customer and their potential, or we've even seen things like with web interactions that you have with a customer, you're able to glean more information that you can then parlay and use in real time to help come up with a more relevant price. So the idea that more information is available there, and there might be real time information that I might want to factor into a price decision that I make is certainly something that we see as well.
Lindsay Duran: So a few weeks ago at virtual Mindshare, we announced the next generation of our price optimization solution. Tell us a little bit more about what that entails and how you're thinking about the next gen of Price IQ.
Pete Eppele: Yeah, a great question. As mentioned before, there's so much more information, competitive information, cost information… companies have more visibility into business that they quote, but don't win, as an example.
And so the [00:11:00] next generation of price optimization really focuses, firstly, on just the ability to consume that information, tools to be able to understand how and where to use that information and then the capability to be able to dynamically sort of work against that information. I'll give you maybe a specific example of that. But as companies, for instance, that we work with, have more visibility into how competition is changing price they might want to look at their price elasticity relative to different competitors to help them understand where to position and how to position in the model to be able to sell. And then you can build on top of that as an example, which is to say that I might position differently against competition in relation to inventory triggers. If I'm high or low on inventory, I want to be able to in a very automated way, be able to respond to those types of triggers, to be able to update my pricing and help shape ultimately my demand and have more control over my revenue and profitability,
Lindsay Duran: Yesterday, I was chatting with [00:12:00] a prospective customer. And he asked me the question of how our price optimization models hold up in times of extreme volatility, right? Over the course of the past year, we had a pandemic. And we're still of course dealing with the fallout from that. We're seeing a very significant inflationary period right now. We're having major challenges across supply chains. That's putting everything on back order. I don't know if you've tried to order furniture lately, but everything takes four times as long as it used to. Can you talk a bit about how our price optimization models have held up for customers over this extraordinary period of macroeconomic volatility?
Pete Eppele: Yeah. Such an important question, because I think that people often have the sense that during volatile times models may not give you a reliable output. And as you mentioned, we've had certainly the most [00:13:00] fertile test ground. I would throw into that two things. Like if you look at what's happening with oil and gas, obviously ransomware attacks, major pipelines and that sort of thing have very quickly moved prices up and down in certain markets. I think this is really where the experience of 20 years of price optimization comes into play in terms of building models that help to understand, and this is a really critically important point, but help to be able to discern changes that are market changes versus changes that are customer changes. An example that I would give you on that front is what you notice during COVID, especially if you look at how much uncertainty we had back in April of last year. Sales were down huge. And it was down for different industries. We work with people that for instance, distribute food and they bring food to restaurants, restaurant business was down and volumes were down in that case.
And so you might think, well, gosh, that would give you a signal that it's time to take prices down. What we found [00:14:00] is with the algorithms is that they did an extraordinarily good job of being able to discern what were just general market moves versus what were volume changes that were price-related volume changes and that's so important. So as to not to overreact or under-react frankly, when you have those volatile times, seeing things as commodities start to move it's really important to understand the relationship between what you charge and the commodity price. When the science has done right and in a robust way, it's able to manage extraordinarily well through those vulnerable times.
Lindsay Duran: And that's really where that elasticity calculation comes into play.
Pete Eppele: Absolutely the case. That elasticity being able to understand what's the difference between a volume change that's more market-driven and a phenomenon of the market versus something that's related to price is really core to that elasticity calculation and unlocks tremendous amount of value in terms of helping you set the right price.
Lindsay Duran: I recall a conversation with one of our [00:15:00] customers in the building products manufacturing space, midsummer, 2020, and one of the things that was uniquely helpful for having the conversation between the pricing team and the sales team was that we handed over elasticity values to the team to be able to have a data-driven discussion with the sales organization, that it was more of a fall-off in overall market demand as opposed to sensitivity to price. And that conversation really helped them keep prices at levels that didn't deteriorate future business, right? Once you lower your prices, it can be much harder to get those prices back up in good times.
And so I think that's really driving home the value of the model and that elasticity piece in particular. So Pete, what else is on the horizon for Zilliant from a product perspective, we had a number of [00:16:00] announcements at Mindshare. Would you like to talk a little bit about our upcoming rebate capabilities as well?
Pete Eppele: Yeah, absolutely. We're very excited to introduce a rebate capability. It's something that we've been talking to our customers and the market about for a long time and with all the innovation that we've had over the last several years with our Price Manager and Deal Manager solution we're very well positioned to be able to help companies with that.
If you think about the price waterfall and you start with, your list prices, you do your on invoice discounting. The rebates really represent that off invoice piece of the profitability picture. And many companies are challenged just simply to execute on rebates. And so we have a lot of capability in this initial introduction to be able to help companies be able to execute rebates, to set those programs up, to be able to do the accruals and sort of connect the dots between the performance that then leads to the rebates and then ultimately to be able to close the loop and optimize [00:17:00] the rebates, to be able to make sure that you are going to be driving the type of behavior that you want.
Lindsay Duran: Thanks so much, Pete, anything else that you'd like to add for our listeners today?
Pete Eppele: Well, I think that as we're coming out of COVID certainly we're seeing the biggest change that we've ever seen in terms of the market and probably an acceleration of the use of technology and customer expectations to be online like we've never seen before for so many businesses. The systems and the capabilities that they had in place sort of pre-pandemic really weren't necessarily built to scale to be able to handle the rate of change or the volume of price requests that you might see when you start selling online and that kind of thing.
So it's a great time for companies to look at technology. To both be able to better execute a pricing strategy, but also to be able to become more nimble. It's a wonderful time if you're ahead of the curve to be able to take business, to provide a better experience to your customers and take business from competitors, and certainly [00:18:00] having an ability to respond quickly to customers with the right price can unlock that opportunity.
Lindsay Duran: Absolutely great point, Pete. I'd like to thank Pete for joining us for this episode of B2B Reimagined. And if you are interested in learning more about price optimization and our Price IQ solution, be sure to visit the link to the Zilliant blog in the show notes, or just visit us on Zilliant dot com. Please also take a moment to rate and review this podcast if you're enjoying what you're hearing, this helps us to continue to be able to put out great content for free, and we hope to see you on the next episode of B2B Reimagined .
