neighbouring, themed speaker,guntan take papers. 45 minutes on our webinar, Enhancing Your Pricing Strategy on How to Unlock the Power of Data and Technology for Pricing Success. My name is Max. My name is Max Bonn. I'm a partner at Implements Dusseldorf Office in Germany. I am very nerdy about pricing monetization strategy, but also about technology. So I've worked as a consultant, both as management consultant, but I've also worked in the industry as a pricing manager and also have been with pricing software firms. So everything we're going to talk about today is reflecting where I spent my time for the last 10 to 15 years. And today I brought my good colleague, Patrick. Yeah. Hi, I'm Patrick and I'm part of the Tech Collective. Tech Collective is a daughter company under Implement, where we focus on implementing tech. And my experience is with CRM primarily, but for the last couple of years, I've been moving over to the pricing space and working with how data and IT can help our clients there. Thank you, Patrick. A little bit briefly about what we do about pricing in Implement is we help company essentially to capture more of the value that you're generating in your everyday value generating processes. And the way we do this is, of course, starting with the pricing and the monetization methods and strategies. But we also have the possibility with our good colleagues from the Tech Collective, such as Patrick, to take it in a broader sense, to take it in a broader sense, also taking care of the data and technology, both helping to select the right technology partners, but sometimes also build solutions ourselves. And of course, we also take the perspective of people and processes. So how can we engage the full organization once we have implemented a new monetization strategy, have implemented a new technology, and now bring that also to the people people in the organization so that it actually creates value. So what are we going to talk about today? We're talking about technology. And the good news is that there is a wide range of technology offerings for pricing in the market. And one way to do this today would be to just go through all of these here one by one and discuss the pros and the cons. And that's not enough. In our opinion, this is not what will get us where we need to be. This is not what will get us on how to gain some value out of that. So we want to approach it in a little bit of a different way. Starting with the why, meaning, you know, understanding why would we actually, why should we engage with technology? Why is technology important for our pricing? Even though I think, you know, all of you showing up here today, we don't need to convince anyone of that. So we'll focus a little bit more on the what and the how. And that means identifying really what are the use cases that you need to support with pricing technology in your specific situation, the way you do pricing, and then what's important in order to successfully execute that and put that into a technology roadmap and execute on that roadmap, implementing the right technologies. So that's what we're going to spend on for the next 45 minutes. But before we dive into that. I would ask you to if you if we can do a very quick round of virtual introduction. And you you can you don't have to do all of the things you see here, but it would be nice if you could at least type your role or your industry in and what's the main technology or software you're currently using for pricing, which, you know, might be a manual calculator, might be Excel or whatever. But maybe we just give it like 30 seconds. So you could type in to the chat and answer as many questions of these as you feel comfortable. Of course, it's also fine if you want to stay a bit more anonymous and take it a little bit down in the information you want to reveal here. I very much like the iPhone calculator. I think that's a very common tool, especially for the commercial frontline when you're out at the client. And somebody is asking for a for a discount. Then the iPhone calculator comes in very handy. I read a lot of Excel, which I think is the backbone of many companies, not only in pricing, but also in other things. But it's interesting, of course, to see that it's a lot also with the with Excel. And I can also see a couple of answers saying, you know, custom tools, which I think is very interesting. And we will come back to that topic in a bit. Because I, from my experience, that's a very suitable way, at least to mix this maybe with some other off the shelf things to gain some value. Thank you very much for this. I suggest we move on now. Thanks for that virtual round of introduction. And let's move a little bit into the topic. Very briefly, you know, understanding the need for change. Why is it that we actually meeting today and that, you know, people are interested in using technology and especially for pricing. I think the main reason is that there are a couple of external factors that drive really the need in companies to use technology. And one is really increasing complexity. I mean, more customers, more products, more channels, and all of that together, just gets to an enormous number of price points. And that enormous number of price points needs to be managed. And that's a combination of rules of analytics, and so on and so forth, which our brain just cannot do anymore. Then on top of that, we have increasing dynamics. So everything, what I just described just happens just happens enormously faster. And then, of course, there's more data available in the market that we can use for data, which provides a big opportunity to gain some more value in our pricing. And on the other side, there's, of course, the pressure from increasing competition. Competition is using technology, but also the technology provides a lot of transparency in the market. If you look into e-commerce, basically, every prices are at at everyone's hands with a blink of an eye. And if you look at the internal challenges for successful implementation of pricing technologies, there are a couple that we need to mention here. The first is, of course, data management. We can have a lot of data if it's available, but it's useless unless we can store it and process it effectively. At the same time, we also need to make sure that the data has sufficiently high quality so that we can actually perform some valid analysis upon it. We need to have some robust systems in place that can store data, clean it and turn this data into actionable insights. Then that's the question of resources and with resources, it's both personnel resources, but also having the right budget in place for having a pricing software, which can be quite expensive. And here we need to consider whether to build our own custom solution, which some of you have done, or buy an out-of-the-box kind of solution. And it's about balance here because investment in technology can really, really help you streamline your processes and cut down on employees spending time on things that are not necessary. So this can really be a big help. Then there's a question of changing our mindset. And it's no longer enough to just rely on gut feeling and outdated models when setting prices. Technology-driven decision-making means that we use predictive analytics and we use machine learning to help us get ahead of our competitors and get ahead of them to curve. So this also means that we can simulate outcomes, we can make all of our decisions data-driven and help us get away from this sticking a finger in the air when setting prices. And then we see a misfit between our pricing needs and what we have of technology. And that is both our current technologies and what we are looking into acquiring. So this is where we need to have a solid evaluation process in mind when we are looking to acquire some new technology. And we need to make sure that this technology aligns with our pricing processes. Finally, we have a question of change management. It's very important to get everyone on board when we go out and acquire a new technology. So it's about understanding and stepping into the shoes of the people who are actually going to use it and about communicating the value and training them. And it's also important to have a well-defined governance process to understand who owns the tool and making sure that the product is going to be able to use it. So this is where we see pricing classes number 119. sellers Hithare is going to need to focus more on digital kaufen and being underutilized. So these are the Silicon Valley as well. can see if we actually manage to do digital pricing situation successfully. And this 2-7% increase in margin, it comes from two things. It comes from both making better pricing decisions and setting the right price to the right customers. But it also comes from the efficiencies that I just mentioned. So going away from this case by case pricing and having humans involved to having a more automated approach. So this will sum up the why of it all. And now we will go through how we can achieve these benefits through our session. Thank you, Patrick. So I think the essential question that I've seen both with having the roles myself or working with clients is that it's a little bit, you know, where do I start? Where is it best to apply the technology? And what we want to provide is a little bit of a framework now on how we can decide where to get started and identify really the correct use cases. So if we very generally think about technology on the one hand side, what can technology do? It can collect data for us. It can manage data structures. It can help us analyze data. It can execute rules. It can avoid data when we're spending data. It can keep ideas and algorithms in a consistent way and in a fast way. And I think, last but not least, technology is the interface between data and humans that helps us to understand or helps us basically as a human to engage with data. Otherwise, that would not be possible. So that's very generically, I think what the capabilities of technology are. And if we then look at the other side, the way we do pricing, what is our pricing? pricing needs. And that is defined by in which dimensions we basically differentiate our prices. Then what data do we have available to do that or to even try for our pricing? And what's the essential methodology we're using for pricing? And we are going to dive a little bit deeper into both of these things now, starting with the technology or the technological capability part, and then finding a way to match that with the right-hand side, with the differentiation data and methodology. So if we start on the technological side, so starting with data collection, what does that really mean for pricing? I think data collection for pricing means gathering all the data you need as an input to make your pricing decisions. And that's a lot of, I think in the best case, a lot of external market data. But of course, I think we should consider that there's also a lot of internal data that we need to collect to make these pricing decisions. But the more we can get to the market, the closer we can get an outside-in perspective, basically, I think the better it is. And if we look at the technologies that are available for that, of course, there are pricing-specific solutions always. But there are also, I call them pricing agnostic solutions that then can be tweaked for our pricing needs. So in the case of data collection, yes, there are price scrapers out there that you can just subscribe to, basically. But there are also toolboxes and frameworks available that can be very easily tweaked to use them for pricing, even though they are not specifically built for that. If we go to the next section, managing the data structures. This is about managing both the pricing data itself, so list prices, contracts, and so on and so forth, but also the input factors that we use for our pricing. And that's all about customers, products, competition, and so on and so forth. And also here, I think there are pricing-specific solutions, so there are integrated price management solutions that help you to manage all of these data structures, that help you manage all of these data structures. But of course, there are also pricing agnostic solutions like databases, data warehouses, lakes, data hubs, or even ERP systems that potentially can be tweaked for your pricing purposes. So there's also different ways to approach this for managing the data structures. If we go to the next one, the data analytics. data analytics. And data analytics, I think, in pricing, essentially, is about finding the right ways for differentiation. So finding the right products or customers to offer, or channels or times to offer a different price. And on the other hand, trying to understand really the price-demand relationships. You could call it elasticity. You could call it willingness to pay. All of these things are somewhere hidden in the data and by analyzing the right way. And by analyzing the right way. We are hopefully able to extract that. And also here, there are pricing-specific solutions. So there are vendors out in the market that offer price optimization tools, where at least there's the claim, which is just, you know, throw in all of the data, and it will come out the perfect price point. But there are also pricing agnostic solutions, like BI systems, or, you know, just data science frameworks in Python or R or something. And then there are also, you know, that can be, you know, that can be tweaked if you have the right skills or the right team in place for the pricing purpose. The next one is the execution of rules and algorithms, which in pricing means, you know, rules for setting list prices or rules for giving discounts to customers. And doing that, especially in a fast and consistent manner. I think that's the thing that technology can help us with that. And the pricing-specific solutions again here are price optimization software is in place and dynamic pricing tools. But of course, there's also the possibility to, you know, do everything from scratch yourself. And I have done that myself before with teams to build home-prone solutions, so to speak. And there's also a bit of a middle ground with no-code platforms that offer the frameworks, but are very, very flexible in the way they are configured and that can be configured by yourself if you have a little bit of a technological understanding. And I think it's about, you know, finding the right fit for your purpose. This is the other thing with this approach, but I timeline and intelligence between disasters and you can't compute initiative. And I think there's many ways that function of the there are again pricing specific solutions like cpq software etc in place um but of course this can also be built from scratch or using some bi tools or other technologies to serve that purpose so what we've uh what we see here what we've done here is to line out a little bit the general technological capabilities how can they be applied for pricing and now we go into the next step thinking about you know what are the needs of the different pricing archetypes which describe the way we do pricing and the way we see this it's it's about three things it's about the way differentiate it's about the data that can be used and it's about the essential pricing methodology and for each of these of these things there is a different magnitude how important that is for for your case i mean if you take for example a e-commerce company that will have very different needs uh if you compare it to a make to order uh project business if you compare it to a spare parts pricing um organization or if you are um if you are pricing innovations so um if we go through this uh differentiation is basically about the the four piece of differentiation so how do you treat your products differently that you treat your products differently that do you price every product the same or do you see differentiation potentials on the product side um how do you serve the prices for different customers is everybody getting the same price or do you do differentiation among your customers on the who um then the differentiation on the where which is about channels locations countries um or uh whatever you know geographical um are you um are you um basically are you tied to internal sources because there is no data outside available that can help you with your pricing or are you in a business where basically all the customer and competitor information is out there available but just needs to be collected and then within that data it's about um the the the three b's of data at the variety so how different are the sources the data objects and the formats how big is the data you know how big is the data you know you know what you need to be in a company in a company where you need to be in a company where i am using data and the data you know how fast does the data you use as an input factor for pricing change and to what extent is it relevant that you you know transform this change of the input data into a price change immediately which has a has, of course, to do with the differentiation perspective of period that we just mentioned. And then last but not least, about the methodology. I mean, there are a lot of different pricing methodologies, but for understanding what the technological need is, I think we can break it down into, is it very rule-based? So are there hard rules that define what the price is? Is it very analytically based? So do you have data sets that you can analyze to take out insights that will then help you to set the right price points? And the last point I think that is very important is to what extent is the price that you're calculating a final price? So is it like in an e-commerce shop that is a take it or leave it price? So the consumer decides, I take the price or I just go somewhere else? Or is it a negotiation game where you have a quoting, a salesperson negotiating with your customer and then agreeing on the price together? And depending on how you scale or how you score on these different dimensions will determine basically what are the use cases for technology use that you can get most value out of this. So if we, sorry, if we now take one specific example for this, we call this archetype the data-driven dynamic price setter, which could be somebody in e-commerce that has a very big portfolio with different price sensitivities that they want to change or price differently. Then on the people differentiation, I think at least in most B2C businesses today, everybody's paying more or less the same price. I think there have been a lot of interesting cases where people have tried to do personalized prices and failed so far. Then in terms of differentiation, I think a lot of, or some e-commerce players are omnichannel players. So there's a big question about, you know, what is my online price versus what is my price offline in the stores? And I think that the thing that stands out for most in this archetype is the speed at which you want to change prices, which is essentially immediately whenever something changes in the market. Yeah. Then going a bit further down, I think on the data side in general, this archetype has a lot of data available, a lot of data that is changing fast. And the methodology used there is based on the data availability also driven, very analytically driven. So the main need, I would say for this kind of pricing archetype is that you have a very fast end-to-end data processing, meaning from gathering the data in the data in the market, you have a very fast end-to-end data processing, which is the main technological need that needs to be considered in that use case. So if we, I mean, I think we have identified something like eight to 10 of these different use cases would be a little bit too much to go through them now all in detail. But what we would like to do is at least for one or two of these, select some of the use cases to go through and say what we mean by that and how that would now tie together with the technological capabilities. So let's take the e-commerce that we just looked at as an archetype and let's select the use case of dynamically setting market-based prices based on competitor data. So starting with the first technological capability, the data collection, what is it that we need to grasp? Yes, we need to go out in the market and grasp competitor data as fast as possible, as efficient as possible with best possible quality. But also there's additional data that will help us to say how we should use the competitor prices, right? There are shop metrics from your e-commerce shop. There's other web data from search engines and there's our internal transaction data. And all of that needs to be brought together. It needs to be managed in data structure. It needs to be cleansed and especially it needs to be brought in a kind of, you could call it relational model that allows you to use it for pricing. And then in the data analytics, which is, you could call it for pricing. And then in the data analytics, in this case, I think it's very much about understanding the price sensitivity per product and per category, which can be derived out of that data if it's organized in the right way. And the second thing, the understanding of the competitor behavior, and then bringing those two things together, the competitor behavior and the sensitivity of products and categories to put that into rules. And now we get to the next point where the technology, I think, can support. Then put up rules for setting the prices differentiated based on the price sensitivity and the competitive data, but also adding additional rules that might not be in the data, something like minimum margin, strategic adjustments, strategic pushes of categories, and so on and so forth. And then last but not least, I think I have a very painful case with that myself. If you have the first four steps lined up very well, and you are able from a price change in the market to get to a new price yourself within a couple of hours. But then it takes overnight to put the prices into your webshop, then all of it is lost, right? So what I think in this case is especially important is that you really have the end to end the end view of pushing through the prices end to end end to the point of sales. So that's one use case in e-commerce. Another one that you could see is a totally different business, a make to order business, where pricing is essentially responsible for identifying target prices for customer product combinations to provide them to the sales force. And what that means in the different technological components. And what that means in the different technological components again is that we need to first gather all the product data and the customer data, which is mainly internal data, but of course, possibly also add that with some market data from outside as well. Structure that and manage our value drivers that drive the value of the products, but also manage the contract and discounts and the rules how to set and differentiate prices for different customers for different customers or customer types to manage that in a proper way in a technology. Then the data analytics in this case, I think it's mainly about understanding the price sensitivity of the different customer segments for certain products to understand where can we charge a little bit more and charge a little bit less. And so the analytics part needs to be really focused on that. So using that information, we can build up rules that are then using calculations to set the list prices based on the value drivers from a product perspective, but also then applying customer specific discounts or customer group specific discounts to get to certain target prices, which then in the last step are presented to the salesperson that is out there negotiating with the customers, quoting, quoting, giving price indications, giving price indications and so on and so forth. So in this case, I would say the last part again, which in my opinion is often, you know, underestimated is a very important one because you can calculate the best prices and then provide the best prices. But if they are not used by the sales force in the way you anticipate it, then all of that is lost again. So this is why we think that last part of the salesperson Let's you mentioned in the example 2. And the last thing that I would like to mention for this is we talk in these use cases very often a lot about the methodology axis. So we talk a lot about what can we do with technology to make better pricing decisions. And that's the Y axis here, so to speak. And I think that's very important. But there is also a second axis. And I think Patrick mentioned that before. And that we have to also consider how can we use the technology to gain efficiencies, gain speed, and gain quality and consistency in executing our price rules, for example, to avoid errors, to become faster, become closer to the market. So it's not only about getting more and more advanced methodology through the analytics, but also the operational part on the X axis here. And what you can see here is one potential roadmap that you could think of, starting with a manual cost plus approach, and then moving along the methodology axis, building an MVP for a market-based or competitor-based pricing. And then you can decide in which technology that can happen. But once you have that MVP in place, you maybe want to think on the other axis. So how can we make this operational now? How can we make sure this is executed in an efficient way? And then once this is in place, you can think about the methodology axis again and say, maybe now I have competitor-based prices, but now I want to add the optimization, elasticity optimization perspective to it. And again, you can build on that and start with an MVP to prove the value to maybe free up some budgets or whatever is needed to then go the execution axis again and operationalize it or focus on on efficient and excellent implementation. And in my opinion, and it's natural that the methodology axis is often very easy to sell to the board, to the top management to get some funding because you have very nice uplifts in profits and sales or whatever your target is. But then it's forgotten that there needs to be an investment in the other axis. And so I really want to make a point here when talking about this, always to have also the further outlook into, yes, we can do something very fast and get some very fast first good results. But then we also need to set some budget aside to make it operational because in my opinion, otherwise what happens is you will put that on the backs of your people in the pricing and the sales departments and having solutions in place that are not liked and therefore underutilized. So this was about the Watch. We talked about what are different archetypes and pricing needs, and what are the technological capabilities and היorts and how can you bring them into a roadmap like the one you see here potentially? and really implement it in a way to gain value from it. Yes, thank you, Max. Yeah, so stepping a little bit further into the how of it all and how you can acquire a tool that actually suits your specific use case. The first thing we need to consider here is the build versus buy dilemma. And we are trying to put it on a scale here where to the very left we have building a solution all by yourself, taking everything in-house, and then going into the middle where you partner with somebody else. You find a partner out in the pricing environment and you customize a solution together with them. And then to the right you have buying an off-the-shelf solution and just using that in its entirety. The custom development solution can be very relevant if your company has some very unique pricing processes that the software should be tailored to. Or if you need to spin up a working prototype that can demonstrate pricing value quickly. It does, however, require that you have some significant in-house expertise to both first build the solution and also maintain it. And it can be very people-dependent. And this is true whether you just build a solution in Excel or whether you decide to build your own high-tech software. Generally, custom development can be recommended if you stand to gain a significant advantage over your competitors by building your own software or in the case where you have to build a MVP for a more sophisticated solution in the future. If you go over to the partner scenario, this is a good choice when you don't have the resources internally, but you need someone to share the risk and build something that is functionally relatively quickly. So the partner could be someone who has their own piece of software, but it can also be someone that you go out and acquire a piece of software and they can help you customize it to your specific needs. It allows you to not having to start from scratch and allows you to build upon something that already works. The partner scenario is also great because it allows you to ramp up on capacity in the short term without having to hire new resources in. At the same time, it comes to risk because you need to manage partners, you need to be a kind of a project manager as well when you do it. Finally, we have the off-the-shelf solution where a lot of companies play right now and try to push these solutions upon us. These can, of course, these can be customized, but they are way less flexible than the alternatives. For that reason, an off-the-shelf solution might be most suitable when your pricing needs are fairly standardized to the industry and when the speed of implementation is critical for you. No matter what, make sure that your vendor is someone that you can trust, that can provide your support, they have a very flexible solution, and that they align with your lawns. So, you can also have a long-term roadmap on your pricing so that you don't end up logging yourself into something, some software that you don't really gain any value from. At the bottom here, we have two examples, two use cases where you can kind of see how this make, partner, buy continuum could work. And the first one is price scraping. So, in the make scenario, this would mean that you built your own web scraper in Python or whatever program language of your choice. You then have to implement all of the barriers that people put online to prevent you from web scraping, and then you have to store it in your own database locally. The collaborative solution would be to find someone, a partner, that can help you scrape the data, but then maybe offer some APIs that you can get access to and then put into your systems. And then the off-the-shelf solution would be to simply subscribe to a pricing scraping platform that takes everything in one place. Another example is elasticity, calculating price elasticity and setting prices thereafter. Here, having to make it yourself would mean that you have to build your own data scientist team to build a model, train it, and host it on your own infrastructure. This can be quite a toll on your pricing team. The partnering option would be to partner with some data analytics firm and use their models to run on your data and then tweak it a little bit to your needs. And then the buying it off-the-shelf would be that you just buy a piece of software that does everything for you. In the end, there's no really right or wrong solution here, but the choice depends on your technological capabilities, the situation you're in, and the reasons that that. resources that you have. Then we have a little bit of a process here because implementing new pricing tech is not just a tech upgrade. It's also about changing the organization and changing the culture. So we are trying to sketch it out in three key steps here. So the first step here is called partner selection. And this can be understood in a very broad term since you can either partner with someone or you can build it yourself, as you just mentioned. I tried to think of an analogy here and I thought that partner selection was kind of casting for a big Hollywood movie. Means that you get out and find the right actors and then align them with your vision and your script. And this goes also along with what Max described around archetypes. You need to really understand your archetypes, understand your needs, understand your vision and your use cases. And then you need to find the right star for the role. So next up is implementation. And if we stick with the Hollywood analogy, this is about directing the movie. So now you've got the cast in place and it's about execution. So making sure that you have a, first of all, a dedicated team of resources assigned for the whole process of implementing this tool and making sure there's eternal alignment around how the implementation process should work. We can really recommend doing one or several pilot programs where you can test the software without tying your entire business to it. So you can go out and negotiate with the partner to have a some kind of pilot program that will be really, really good. Because a lot of times we have seen that customers, they go out, they acquire a new big and expensive piece of software and they tie it into all the systems and they end up being locked to it and not getting any value for it at all. So try to negotiate with the partners. So again, going back to the Hollywood analogy, this is similar to having some screenings in the early stages. And if the actors don't test well with the audience, then we can go back and recast it. And finally, finally, finally, we have the very important step of driving the change. And this is like the grand release of our movie. Now everybody needs to be excited about it. We need to go out, provide some good trailers and hype the movie. So in this case, we have to go and communicate the benefits of this new technology. We need to train the users and really facilitate adoption of it. And here it can be good to understand that technology is not just affecting you in the pricing department, but it's not just affecting the pricing. department, but really affecting the whole organization. And for example, especially the commercial team. So try to put yourself in their shoes and understand their perspectives and be ready to gather some feedbacks and address the challenges that you have. So set up some systems for that. And we won't dive into it here, but of course, we at Implement have some real nice tools that can help you with this process. Finally, summarizing a bit, Max. Thank you very much. Thank you very much, Patrick. Yeah, as a, as a summary, we'd say there's the first thing that we want to emphasize is don't start with the technology, but start with your strategy, understand what your needs are, and then go out and find the right technology and partner. Don't do, you know, technology driven decisions, but rather do it the other way around. By doing so pick what fits your organization. There is no generic right or wrong between building things yourself or taking off the shelf solutions. But it really depends on the maturity of your organization. And there can even be a good mix of off the shelf solutions together with homegrown solutions, depending on, you know, in which area you have the right capabilities and the right skills available. And last but not least, and I think I cannot repeat this often enough. Don't go full in in the all in the beginning, but test and learn in agile sprints. Start with minimal viable products. Also, if you go out with partners and, you know, do a partner selection, make sure to not log in within a three years SAS subscription before knowing if things work, but start with a pilot evaluated properly and make sure you also you know, you know, put this in the, you know, put this in the contracts so that you could get out in case the technology is not giving you the value. That is what we had prepared for today. Thank you very much for taking the time. Thank you very much for, for leaning in and diving in. And thank you for being allies on using technology to make better pricing decisions and become better in pricing. We are very much looking forward to you reaching out if you want to learn more. And we hope to speak to you soon and maybe engage with some of the questions a little bit more in detail that we discussed today. Thank you very much. Thank you.