Lindsay Duran: [00:00:00] Welcome to B2B Reimagined. My name is Lindsay Duran, and I'll be your host for this episode. Joining me today is Todd Pate, Zilliant business solutions consultant. Todd, thanks so much for being here.
Todd Pate: Hi, Lindsay. Thanks for having me.
Lindsay Duran: Before we get started, Todd, why don't you tell us a little bit about yourself your background, and what your current role is with Zilliant.
Todd Pate: Sure, Lindsay. I have been in the broader pricing space for more than 20 years. The vast majority of my career leading pricing organizations developing pricing process, implementing tools, and building out pricing teams in a variety of different companies - most of them in manufacturing in the high-tech space. I joined Zilliant earlier this year, specifically with a focus on manufacturing and high tech. As a business solutions consultant, it's my role to make sure I understand our prospects and customers, the linkage between what our software capabilities can do, [00:01:00] and the specific and particular problems that they may be facing in their unique business.
Lindsay Duran: Excellent. Well, it sounds like we have the right person on for this episode. Todd as our topic is high tech today, and the challenges inherent with pricing for high-tech companies. You recently were a presenter on a webinar with TSIA, which is an industry association for technology services companies. Can you tell us a little bit about the topic of that presentation?
Todd Pate: The topic was specific to high tech, and we're really focused on the ever-increasing complexity around the creation of offers. High-tech companies are under more pressure now than ever to put together more tailored and customized offers for their customers. I think this is a general commentary about just industry trends, but I think it's particularly tip of the spear in terms of how [00:02:00] it's felt within the high-tech space. Customers demand really specific and tailored responses to the types of products and services that they need. So as those domains have continued to increase within the high-tech space, the ability for those companies to respond to those offers. Do it in a way that still provides response that's complete in terms of the totality of what's included in the offer in terms of how accurate the details of the offer are and the speed at which they can turn it around. Those things sometimes come into conflict with one another. So, the particular focus of the webinar was discussing that as a problem, and what some of the current best in class solutions are that are being developed to help solve that.
Lindsay Duran: It kind of goes to that old adage - I can do it right, or I can do it fast.
Todd Pate: Yeah, exactly. I could do it right. I could do it fast. I can do it cheap. Pick two of the three I think is the adage. It's very similar to how when you're asking these organizations that again, as [00:03:00] complexity is increasing, I can put an offer together for you. It can be complete. It can be accurate. Or I can do it quickly. But I can only do two of the three, which of the two you want me to tell you? The message that we had in the webinar was you could really do all three if you've got the right process and tools to be able to help you execute.
Lindsay Duran: Let's break down each one of those legs of the triangle if you will. Let's talk a little bit first about completeness. What do you mean by completeness of the offer in the context of high-tech companies?
Todd Pate: A lot of high-tech providers are moving to more complex solution selling. They're moving, evolving from a classic product model to more of a solution or a services model. Often times the way that they monetize reflects that with moving towards more of a subscription-based model for how they collect revenue. As companies are evolving and putting together these new offers. Each one requires a certain level of tailored elements that go [00:04:00] into that offer. What often times happens is, as the offers are being put together, there might be pieces that are left out. So when we talk about completeness, it's am I actually putting the offer in front of my customer that's going to resonate with him or her the most? Is it the one that's going to differentiate my solution that I have to bring to the table versus what my competitors are offering. Am I not only asking for and providing what they're asking for directly, but am I anticipating some needs that maybe aren't being asked for directly? There may be elements, because I'm an expert in this space and an expert in the industry, I may know better than they do in some cases what they really need in terms of a solution. Am I bringing all of that in order to put together a complete offer?
Lindsay Duran: I would imagine Todd that that also extends to how do I make sure I'm giving a complete offer to my partner or channel resellers. A lot of these high-tech manufacturing [00:05:00] companies, I'm thinking the likes of Cisco systems or Polycomm, do quite a bit of business through the channel and may have partners that are registering deals for them or asking for special pricing. How do you see that completeness of offer extending to partners?
Todd Pate: When I use the term customer, I use it generically to mean either the final end user of the product or working with a channel partner who's an intermediary that may have a more direct relationship with that end user. Part of that bias is from my prior experience in running a pricing group at Dell computer, which is famous for executing a direct model within customers. But I certainly have had a lot of other experience with high-tech manufacturers that sell through value added resellers or other partners that are adding back to this notion of solution selling. So, it's sometimes even further complicated. It's not just the manufacturer that's having to build out a solution sale. They're doing it in conjunction with a [00:06:00] partner who's adding some additional value to the solution, and they are collaborating on how they combine those elements to ultimately put together a complete offer. When you think about the completeness of offer, sometimes it is a manufacturer dealing directly with that in a consumer of the goods or service, but in a lot of cases, it is working collaboratively with a distribution partner or evaluated reseller to build out that complete offer.
Lindsay Duran: Absolutely. Let's switch gears and talk about the accuracy of the offer. Can you dive into that and tell us what you mean by that term?
Todd Pate: There's probably a number of ways that you can measure accuracy through our lens, and obviously through my prior experience in the pricing space. Do we get the pricing right? Is did we put a series of prices on the offer that we're putting in front of our prospect or customer that has the best chances for us to win the deal, as [00:07:00] well as optimizing some combination of revenue and margin or profit once we've actually won the deal. In terms of what we presented in the webinar with TSIA, the main metric we use for measuring accuracy was setting the right price.
Lindsay Duran: Which is often easier said than done. We'll talk in a bit about how companies can do that. But before we get there, let's talk about this last leg of the stool and the concept of speed. Why is that so important in helping companies win more business.
Todd Pate: I think speed is actually the most interesting of the three. I think it's the hidden factor that is going to be a key differentiator for a lot of success, particularly in the high-tech space. I've seen it in roles that I've had when I've been running and managing pricing desk and pricing teams responsible for actually getting responses back to either an internal sales team or directly to a customer for an [00:08:00] opportunity or if I've been in a capacity like I am at Zilliant where I'm working with clients. Time and time again, you see the single biggest factor that leads to benefit with making enhancements in how you respond to offers is simply the reduction in the amount of time it takes you to get an offer back. It's often times not the best offer that wins a lot of these competitive deals. It's the first good offer. So speed is critically important, and I think an underappreciated component of what makes a good offer.
Lindsay Duran: I think some of our experiences as regular consumers outside of the business world bleed into our expectations in a B2B context. You and I, if we're shopping somewhere online or in a store, we don't expect that it takes multiple days or even multiple hours to know what the price of an item or set of items or services is likely to be. We get that pretty much [00:09:00] instantaneously. So, I think those expectations are increasingly seeping into the B2B world and, the days of taking multiple days or a week to turn around a quote is just no longer acceptable.
Todd Pate: No doubt. If you choose to do that, you're going to put yourself at a disadvantage. I think that's particularly true in general B2B. I think all your comments are absolutely true. As a consumer preferences and tendencies tend to creep themselves into the way that decisions are made in B2B buying. But I think it's even more true in the high-tech space. I think those demands are even higher. I think the expectation when most customers are dealing with high-tech providers is that they are going to be able to provide them with that type of more modern consumer experience that you referenced Lindsay.
Lindsay Duran: It sounds like with those three challenges, speed, accuracy, and completeness that companies might find themselves in what we'll call [00:10:00] a whack-a-mole dilemma. As soon as they attempt to fix one, they inadvertently break the other. But there is a way to address all three. So, let's start with the accuracy component. Why don't you talk a little bit about how companies can address setting better prices up front.
Todd Pate: Before I get into any one of the three, accuracy for example, let's talk if there is a fundamental framework that's applied that allows for improvements on all three simultaneously, which is why you can avoid the whack-a-mole dilemma. The issue that we discussed earlier, Lindsay, which is pick any two of the three and tell me which two you want. I can't deliver on all three. If you bring data science to the table, and you're able to utilize all the embedded nuanced information that is already there in the rich set of data transactions that you have. Attributes around the types of customers that you're doing business with, and the types of products that you're selling. There's a lot of [00:11:00] information that's embedded that can be used to help create a segmentation framework that is purpose-built for creating offers for optimizing those offers for creating prices, optimizing those prices, and being able to do it in a way that constructs deals. Not only optimizing what's in it and how it's priced, but also does it in a way that speeds up the process considerably.
Lindsay Duran: On that front, how can data science specifically be used to address the pricing offer? In the situation that I'm really envisioning here is let's take that value added reseller that we talked about, and they're in a competitive deal to win business and they go back to the main supplier and are asking for competitive pricing. How can that supplier, that manufacturer make sure that they're getting the right prices into the hands of that [00:12:00] VAR?
Todd Pate: So, by utilizing a data segmentation framework and by understanding when you get into a particular competitive situation. If you have no certain critical information, and this is all again, assumes that you've done some the necessary pre-work. That you've run the statistical models on your data sets to understand which attributes are most significant in describing when price is differentiated. When a individual situation crops up, and you know what the attributes are that pertain to that particular situation, such as who's the partner involved? Who are the customers involved? What's their end use industry? What types of products are they buying? How large is the customer? What part of the world are they in? Those types of attributes can all be used to appropriately slot that transaction that's being evaluated, and the request is being made by the VAR partner against a peer group of similar types of transactions. [00:13:00] Similar products, similar customers that will yield price recommendations that say given what we know and what we've seen historically. Now that we've identified a peer group that is very specific and very relevant to the transaction that we're evaluating. We have all the relevant information we need to come back with a recommendation on do we need to be aggressively priced or conservatively priced in order to not only win the deal, but try to optimize our revenue or margin for this opportunity.
Lindsay Duran: Now let's say we get that price into the hands of the reseller, and they find out from the end customer that they need to be a bit lower in order to win the deal. How can companies streamline the negotiation process as the reseller goes back to the supplier and asks for a price concession?
Todd Pate: I'm glad you asked, Lindsay. That was another key component of what we discussed during our recent webinar with TSIA. Speed is not just in putting together that first quote, [00:14:00] but it is just a first quote, right? It's a much better and precise first quote, based on all that history. But with a peer group where it is still a single data point. There's still gonna be some variability even within that group of light transactions. So, you have to open up the door and leave the possibility for some level of negotiation or counter offers to come back from that initial offer. So, how can you improve the speed of that process as well to get to, not just an initial offer more quickly, but get to a final offer more quickly. In terms of the solution on how you do that, again it uses a lot of the same techniques. Once you've engaged a particular opportunity, there's a science to negotiation itself. There's a science to how offers and counter offers can work back and forth. That science can be administered programmatically through artificial intelligence in a way that has from the experience of the party that you're negotiating with, whether it be a partner like a VAR directly with an end customer, it feels to them as if they're negotiating with another person. [00:15:00] But in terms of how it's administered on your end, you're actually using and embedding negotiation science into that process to be able to speed up the rounds of negotiation and to be able to get to what would be a final price much, much faster than if people were involved. By the way, it's not only faster, it's also much more consistent. So, it creates a better experience particularly for customers or partners that you're dealing with on a regular basis. They're not scratching their heads wondering why they got a much different outcome from the current negotiation than they did the last three negotiations that they may have had with you.
Lindsay Duran: I think that's a great point, Todd, and it's really taking eCommerce to the next level for a lot of companies, right? It's one thing to serve up prices on an eCommerce site and be able to give that initial quote, but to be able to handle that negotiation intelligently really seems like a competitive advantage for businesses [00:16:00] that implement that first.
Todd Pate: Again, I'd like to point out some of the differences I think with high-tech manufacturers in particular. I think they tend to have more customers that utilize them through some sort of self-service portal. The entire world in B2B is moving that direction - through omnichannels. But I think high-tech is their first and will continue to drive more and more volume through that eCommerce platform. So, it's back to the part of the conversation we had earlier, Lindsay, which is how do you make the consumer-type experience that we all have when we deal with eCommerce today? How do you replicate that type of frictionless ease and convenience when you're dealing with a B2B portal, which can sometimes be a little bit clunkier. More opaque in terms of not only what you're buying, but how it's priced, and how if there is an opportunity to be able to offer push back or counter - how that's being negotiated. That's a perfect environment to execute something like [00:17:00] automated negotiation intelligence. To be able to have that conversation automatically. To be able to calculate what the next offer should be, and to be able to arrive at a final price even faster and do it all through eCommerce. The great thing about setting it up for an eCommerce channel is, at the end of the result, that you still can't if the gap between what the customer's willing to pay, or at least what they claim they're willing to pay, and what the artificial intelligence engine says that the minimum price should be for this particular negotiation. If you come to an impasse there, you can always kick it to a real person. You can always turn that opportunity over to someone in your sales organization to be able to reach out and initiate a human to human contact to complete the sale.
Lindsay Duran: That's a great point. There's always still a role for the salesperson. I think moving some of that negotiation to a self-service portal, as you said or an eCommerce platform, especially for [00:18:00] maybe smaller deals can free up time for salespeople to not haggle over price on smaller deals. Really give them the opportunity to focus on growing accounts on the more strategic, larger deals that may require more of their attention and focus. On that kind of note about growing accounts. It strikes me that when a salesperson is potentially out of the picture in a self-service portal or an eCommerce platform, you might miss an opportunity for them to identify another area of need where another product or service might be beneficial in the context of that sale. How can companies enable those types of personalized recommendations to really make sure the offer is complete from a solution perspective?
Todd Pate: Well, data science can help in that arena too. You touched on really another side of this three-sided triangle that we [00:19:00] described when creating an offer - accuracy, speed, and completeness. Data science can be used not just to help with the accuracy and with getting to an answer faster, and therefore helping with speed. But data science also, through that peer group comparison, can profile a particular customer and say, "based on your buying behavior and based on the attributes that make you who you are, we can slot you and compare you to similar types of customers." We can identify gaps between what your actual buying behavior is versus what other like customers that look a lot like you and that are in your same peer group are buying. Then, proactively serve up those recommendations. What's you're really effectively doing is narrowing all of the solutions and offers that you could potentially bring to the table for a given customer. You're really serving up those that have the highest likelihood to resonate. The highest likelihood that they're actually going to be interested in purchasing because you've seen through data [00:20:00] science like-customers are buying those same types of products and services today. That's more growth oriented, which I think is the real power of data science. The simpler problem it can solve is also looking at gaps in what that customer is maybe no longer buying from you. So, as their buying behavior changes over time, and they stopped buying, are we losing a portion of that business to one of our competitors? Are we losing wallet share in the account and identifying that early is a great way to defend against customer churn for a lot of companies in the high-tech space. As solutions are evolving, you want to make sure you've got a keen eye through the use of data science on identifying those areas that are slipping away and have early warning indicators go out and proactively suggest that you bring them back into the fold and regain that portion of their business. Because if you don't, studies show that eventually you'll lose the customer altogether.[00:21:00]
Lindsay Duran: Todd, is there anything else that you'd like to share with our listeners and subscribers today on how data science can really enable high-tech manufacturers to optimize their commercial processes?
Todd Pate: I just would like to say to anyone who is having run many deal desks over the course of my career, it can be overwhelming at times. The demands are typically high-pressure. It's high turn around. You've got high demands from both internal and external customers to return responses quickly. Sometimes it's easy to beat yourself up for feeling in the zeal to be able to get a response back that you're not delivering the best answer possible for the business. That you're leaving opportunities or money on the table for not making sure what you are offering is a complete nature and has the best possible pricing involved. My message to anyone who feels that pressure today is that data science is a great [00:22:00] tool and a great ally to be able to help, not only improve the speed at which you can get all those offers back to your internal and external customers, but to do it in a way that gives you the confidence that you're really giving the best results possible. So, if you're not utilizing data science and data science tools today, I would highly encourage you to do more research on the topic as a solution going forward.
Lindsay Duran: Thank you, Todd. Really appreciate you being here today with us. To learn more about this topic, I'd encourage everyone to check out the episode notes for a link to our recent virtual event that we referenced today. You'll also get an opportunity to hear Jamison page from Schneider Electric talk about their real world use case of the automated negotiation capability that we talked about today. So, hope you will join us for our next episode of B2B [00:23:00] Reimagined. You can also visit our website, Zilliant.com to subscribe to the podcast to make sure you're notified whenever we release a new episode.
