Welcome to today's webinar. My name is Frank Ulmerich, partner business manager at SAP Emarsys. And I have the absolute pleasure to host this webinar and lead us through the program. We are super excited to talk about why customer engagement has become the primary driver of pipeline, customer lifetime value, and revenue predictability in B2B, because we believe that the future of B2B growth belongs to companies that don't just sell, but continuously engage, because real, data-driven customer engagement has become the foundation of scalable pipeline, durable customer lifetime value, and truly predictable revenue. Please let me quickly introduce our speakers and our special customer guest today. I'm super proud to welcome Svenja Kallweit from ERCO. Christian Schmoliner from Syskoplan Reply and Jason Hayman from Portaltech Reply. With that, we have a packed agenda today. We have a case study-- how ERCO accelerates marketing and sales collaboration together with Reply. We will also talk about-- a little bit about from our special guest, Svenja, how ERCO is going to resolve their challenges in B2B to optimize customer journeys. And we will also talk about the role of AI in B2B commerce, which will be delivered by Jason. And last but not least, a panel discussion to really learn about best practices from the field. And we will have enough time to talk about what's next and answering a couple of questions and giving answers to you. With that, I'm happy to hand over to our fantastic team, Christian and Svenja, and talk a little bit about the current state of B2B. Thanks for your introduction. Happy to be with you today. Yeah, let me briefly set the stage why it is so important, especially in the B2B sector, to bring marketing and sales together. As you can see here in this circle, usually, everything starts with the email. There's a new contact coming in from a web form, for example. And in that case, you get already some new insights. So what you do, and especially in the marketing, you send out and start sending personalized emails. You get feedback by clicks, by openings, by getting other insights. And of course, these insights are very helpful for the sales team to take over in their daily business, in their follow-ups, with the customer-- the potential customer. The sales now knows insights and can start building the personal relationships and, again, add new insights to the profile based on visits, on calls, on emails, et cetera. So of course, this data needs need to be available for marketing to again optimize their campaigns. And so let's say to have the success in B2B business is not only a part of marketing and not only a part of sales. It's the combination. And here, we from Reply have created a solution that combines both worlds. And yeah, this is what Svenja here from ERCO will give you some more practical insights and concrete examples from her-- or from ERCO's daily business. So handing over to Svenja for practical insights. Thank you. My name is Svenja. I'm working for ERCO now 16 years. I'm based in Germany in a small city called Lüdenscheid, and we have the only production building. So we are delivering worldwide, but we are only producing in Germany and Lüdenscheid. And what do we do? We are architectural lighting. That means everywhere where we need artificial lighting, inside buildings, outside buildings. And you have here three examples. It can be a library, like on the bottom left photo. It's a library in Stuttgart which we call a community project. Or it's a culture project, a museum, or here, in this case, Louvre-Lens, or on the right side, an example of a cathedral which we are lighting. So everywhere where architecture lighting is needed, this is where we play. And this gives us also some challenges, especially when it comes to marketing and sales. For example, we are involved in those kind of projects from an early, early, early stage. So we talk to architects about artificial lighting even when the building is not there yet, even when we are just on concept and design. We actually work in a B2B2B model, because the architect is not the end customer, and the end customer is not the owner of the building. And the end customer is also not the installer, but also the architect is not doing the planning for the lighting. So we have so many stakeholders over the lifetime of a project, which can actually last three to five years, from the idea of a new building until the realization. I even have a minor projects where we're waiting for the final delivery of luminaires for 7, 8, 10, 12 years from the idea until finishing or the opening of the building. We have a stakeholder change. I just mentioned that before. From the beginning, the architect has an idea of a building, then an installer is coming and is actually installing our luminaires. And then you have, for the examples you see here in the pictures, for example, a curator in a museum who says my exhibition need to be lit in this particular way. So stakeholders are changing over the lifetime of a project. So in summary, we have a high-end market, and this is a very demanding market. And yeah, these are the challenges which we need to overcome in our marketing campaigning business but also in sales. That brings us to the solution, which Reply offered us. We have here a screenshot from our live system of our CRM. It's an SAP Sales Cloud, version 2. And we are using Emarsys in the background. And what we built in here is the so-called marketing insights app. I'll guide you through that very shortly. On the first point, the first green arrow on the top-left, you see that my sales, opening the CRM, a contact person can directly see do this person has an opt-in status? So is the consent status given? It can also say, hey, this person is interested on a gallery campaign, or a museum campaign, or whatever-- library campaign, if that's ever happening-- and can, by one click, decide, ah, I had a meeting with this customer-- I had a meeting with this contact person, and he seemed to be interested in what is coming up in our campaigning schedule. On the top-right, you can also then, as a salesperson, decide if this person is particularly interesting for a certain target list. So tradefair invitation, the next webinar, whatever is-- yeah, a next touchpoint available. Also the marketing interests, like indoor lighting, outdoor lighting. There are several opportunities also here. And last but not least, really cool-- one of our party moments when we implemented it. You can see here-- that's the last arrow on the left side-- the latest email which has this customer or contact received. And as you can see, it's just the name. But if you click on it, tah-dah. Then, you see there is a preview. And that was really something lately installed and very appreciated by the team. So you do not see only the name of the email, the name of the email campaign. You definitely see a preview of what the customer has seen, and that means you directly have an icebreaker next time you see that content. Hey, have you read our email about blah, blah blah blah blah? In the other system, the Emarsys system, we also implemented some new things. For example, in the on-behalf emails, on the bottom of the email, we can include the respective salesperson with name and contact details rather than just saying here, echo@info whatever. And this was also a very, very positive received feature. As well as nurtured leads were also very important to us. And nurtured leads were working in the past, but it did not work in the way we wanted it to be. So leads were nurtured, but now we nurture them, and we give the information-- what we nurtured, why we nurtured, what was the trigger point? Also, back to the CRM system, so sales can see what is behind. And this is a big advantage for us now. Last but not least, as I told you about our challenges, of our projects, of our construction buildings and projects we are currently working on, this information is needed in the Emarsys when you're talking about making a target or segmentation, or you want to see who is receiving that. We want to know if this contact is already touched by us, if there's something running in the background. So we now play back this information from the CRM into the Emarsys. Also very big advantage for my marketing team. Yeah, last but not least, you probably want to know about KPIs. I am not a KPI person, I have to say. I'm good in talking and not so good in figures, but I can give you some insights. I can tell you 90% less questions from salespeople. We have 200 salespeople worldwide who have asked, what was our last campaign about? What was the last email you have sent? And now, they can find that information in their daily system, in the CRM itself. And yeah, definitely less work in the marketing and also less work in the marketing for ad hoc emailing. That also means less days in administration for collecting feedback, from sales, or target audiences. And it all together created a momentum. It created a momentum where marketing and sales work together, and where I'm now pushing to get more changes done because this is a big advantage. So give me some more time, and then maybe I present next time more KPIs. But I can tell you it's fun. It's really party time. It's working. Wow. I mean, this is a concrete-- a real-world example of transforming forming B2B. And as I've learned from you just now, it's from B2B2B, which is even more complex. And B2B engagement without a complete replatforming or adding additional complexity in opposite. I mean, when we hear minus 90% less questions from sales, that's something which is really great. So with that, I would hand over to Jason and talk a little bit about the future of engagement. So the role of AI is impacting all of us. And especially the role of AI in B2B commerce is something which I'm really looking forward to hear from Jason. Yeah, thanks, Frank. And I'm going to, today, just talk about what we're seeing within the market and the market disruption, and AI's impact customer experience and commerce. So AI is transforming how customers discover, purchase, and engage with an organization. Classical transactional sites are being replaced by AI-powered, intent-driven experiences. Competitive advantage shifts to data orchestration, personalization, and adaptability. And the legacy monolithic systems are starting to give way to composable solutions. API-first ecosystems, enabling organizations to have best of breed commerce solutions across the board. There's an opportunity here. We need to guide our clients through this transition and how they integrate AI into existing composable commerce architectures. B2B organizations want the same experience and benefits as B2C. That's a growing trend within the market. They want to actually understand their end customer. In a lot of cases, they don't have a data insights to do that more for this. And it's more of about how you standardize the touchpoints across an organization as well. So if we have a look at some stats around retail and B2B landscapes and how they're evolving towards autonomous, contextual AI experiences, today's consumers are demanding tailored and frictionless journeys across every single channel, creating both challenges and opportunities for the forward-thinking retailers and B2B organizations. So 71% of consumers now expect personalized shopping experiences. They want to feel special. They want to feel like they're your only customer. 60% of shopping carts are abandoned due to checkout friction, something that organizations need to change. And 100% real-time adaptive experiences are being generated through agentic AI and components. So the vision is to have a unified, intelligent ecosystem, where every interaction feels individually curated. Anticipating the customer's needs before they arise is extremely important. Make them feel special. Make them feel the only one. So where AI is already influencing what we see in the market is around personalization, those tailor recommendations and dynamic pricing, giving customers what they want with a price that encourages them to buy. It's about product discovery. So AI-powered search and catalog optimization-- really important that you find the products that you want quickly and you get to checkout quickly. And it's about the whole digital experience with things like the introduction of chatbots, predictive engagement, and the omnichannel journeys that are seamless. So we're going to have a look at a use case here. And it's a global electronics reseller, and in both B2B and B2C markets with very established centralized digital infrastructure that manage manages their product catalogs, content delivery, and order fulfillment across multiple markets. Now, their challenge was they had a fragmented personalization efforts. They had inconsistent customer experiences and significant friction in navigation and checkout process, which was limiting the growth potential. So the opportunity was to introduce agenetic AI to enable that real time personalization and dramatically reduce the journey friction across both digital and physical touchpoints. So the problem definition and the customer goals-- so the real pain points were pretty low engagement. Static recommendations were failing to capture customer interest, resulting in missed conversion opportunities, therefore missed revenues, decreased sales. Data silos-- they have fragmented customer profiles across multiple systems that prevented a holistic understanding of their customer. They had a high dropout rate, significant abandonment rates during product discovery and the checkout stage. And they had limited accessibility. So there were a lot of manual content curation that restricted their multimodal interaction capabilities. Their goals under this program was to introduce tailored experiences, so delivered that personalized content and promotions that actually resonated with the individual's preferences. It's all about intelligent navigation, so to simplify the product discovery and anticipate user intent throughout the whole of a journey. It's about multi-modal access, so enabling seamless interaction through chats, voice and interfaces. And it's also about the unified touchpoint, so creating a consistent experience across all digital and physical channels. So the solution mainly split down into five areas. So with the AI or the agentic AI layer, you have to make it function like an orchestration engine, seamlessly connecting those centralized systems to personalize and optimize every stage of the shopping journey in real time. So the core capabilities of solutions around real-time personalization, so unified data platform that delivered individualized experiences instantly. Was about predictive optimization, so looking and anticipating the customer's needs with a proactive journey throughout the whole life cycle. So about multimodal interaction-- so support through chat, voice, and visual dashboard interfaces. Both areas around omnichannel intelligence, so seamlessly integrating across digital and physical stores. And the last area is about autonomous learning, so continuous optimization through self-improving algorithms. It's a really important one that, when you're doing anything that involves machine learning, your algorithms are continually updating themselves to deliver the latest data to you. Throughout this program as well, they also realized that they could use AI to proactively recommend what they called the smart home starter bundle. So this was a solution where they would see customer's order history, their browsing habits, et cetera, and different sets of-- or different types of equipment that they're looking at on the website. Now, by bundling them together, it not just increased basket size, but then enabled them to adapt promotional offers in real-time as a customer explores complementary products and expresses their interest through the natural language queries. What does that mean? It means giving your customer what they want, recommending alternative or complementary products to go with it, to make them satisfied and come out. At the end of the day, every business wants to increase their revenue for the basket size. So the adoption infrastructure is very simple. And with any AI program, I would say start with something that's critical in terms of time to realization of the value. So simple four-stage strategy around the foundation, which was integrating the customer, product, and order data sources into a unifying platform. But also looking at the data governance and quality frameworks around that. It's really important that when you're using AI, GDPR should really be at the top of any data program, because you don't want to be misusing that data. Phase 2 is around the enablement. So deploying of the personalization models and launching of the multimodal interaction prototypes. Again, this is all about control testing with selected customer segments. About optimization-- so about activating machine learning feedback loops and implementing the continuous A/B testing protocols. So this is all about refining algorithms based upon the performance data. And phase four is around scaling. So that's extending that AI Logic to in-store experiences and complete omnichannel integration. Once we got through these stages, it is ready to roll out to a more enterprise-wide deployment. So what were the business case metrics from this? The strategic implementation of agentic AI delivers transformative business outcomes across key performance indicators, fundamentally reshaping the customer experience and their operational efficiency. So in this case, the metrics were they want to see a 15% to 25% conversion rate lift, 10% growth in the order value, 20% net promoter score, point gain, and also 30% efficiency improvement. These projections were based upon conservative estimates. At the time, it was done in the industry benchmarks. But the early pilots of these have already demonstrated the potential to exceed these targets, with some segments showing conversion improvements approaching up to 30%, as an example. Whenever you're starting on any sort of AI projects and introducing it to an organization, it's really important that you get to work with a customer or an SI that is used to doing this and helping guide the customers through the journey. It's also important to note that there is a degree of change management within any AI program, and you've got to bring the workforce along with you whenever you embark on this. So it's all about putting your hands in the future of the right partner. And I must say, and I will re-emphasize, partner selection is critical here. Frank, I'll hand back over to you. Awesome. Wow. I mean, what we just learned from Svenja and Christian that a comprehensive system must not be complex. You gave us an insight that you really have to consider a few things to be successful. And what you delivered is nothing less than a roadmap for responsible retailer interest- focused AI and not hype. Thanks for that. So with that, I would like to open our panel discussion and get some insights about lessons from the field and how that turned out and worked out for ERCO, for example. So Svenja, if I may ask, what was your internal "aha" moment? So things that seemed small but actually made a huge impact? Yeah, good question. When we implemented Emarsys and just then got a new CRM update, we were in front of two systems which did not want to talk to each other. And the feedback from SAP consultants were no, and no, and no. And then out of nowhere, actually, Christian came and said, I have a solution. We have a solution. We know your problems, and we can help you. And that was a big "aha" moment, because every time he showed us something, we were sitting in front of our screens with big eyes. And we're like, wow, that's exactly what we need. This is exactly what we are missing for months now. And yeah, so party time was big. Wow. Again, so what did you do to ensure adoption? I mean, especially by sales teams who already have a lot of workload, and are typically not that technology-focused, and maybe not so open for changes, what was your way forward? I mean, if you implement a new system for salespeople, you always get a no. So who's working in that environment knows that. So it's not about how we do it. It's about why do we do that? Why do we need it, and why do we actually decided to go this way? It is still today a communication campaign. It's a change, and it's a huge change. And therefore, talking, talking, talking is the only thing I can give as an advice because that helps. And some say they see the impact. They see that it's getting some impact on their daily work. And once I convinced them, they are my ambassadors. And if I win one ambassador, then I win the whole sales force. Awesome. And Christian, for organizations who wants to start this type of journey, what is-- how should I say that-- the smartest first move they should consider? So yeah, from our experience the smartest move is also that-- Svenja said before-- bring sales and marketing together in one room and define the benefits, the requirements together, because both teams will work closely together for more collaboration. And so it's don't act in silos and get decisions together, because then from there, it's easier to take the next steps and finalize the project or the aim. Yeah, and Svenja, I mean, you are a B2B. So my question is, where do B2B companies typically tend to overcomplicate things? What should they keep simple, from your experience? I'm based in the German middle-sized company, which means we love it complex, and we love it complicated, I can tell you. But that's basically my job. My job is to translate the pragmatism of sales into IT tech language and make it happen-- just make it happen. I love complex things, but I hate it if the solution is complicated. So at the end, it's stop piling up PowerPoint slides-- just start doing it. And Reply was the right partner to do that. Awesome. So because you mentioned Reply, Christian, what is the typical time to value when we want to define what's our best next step? So I think one of the questions is definitely what time to value can you consider to see across B2B projects like this? Yeah. In many companies, the first benefit starts with talking to each other, bringing two teams together. This is sometimes hard enough. But to be honest, once we have installed the solution and the benefit or value starts from day one. The solution is very easy to use. And yeah, especially for ERCO, we track the time before and after go-live. And yeah, as we saw here from Svenja's information, KPIs are coming very quickly. So in these projects, we are bringing together a lot of data to get the right overview-- 360 degree about our customers. And one of the questions is, do organizations need CDP to start, or can they scale without one? So they do not need a CDP to make the solution happen. So we bring both systems together with implementation assets. And so there's no need. And usually, if you already have it Emarsys and/or Sales Cloud V2 or V1, there's nothing needed anymore. So we have a lot of contributors in these projects. How much internal IT involvement is required for this solution from your experience, Christian? As we bring templates, they just have to be adjusted or slightly updated by the IT. So it's kind of integration packages, for example. So the work from the IT is quite low, I would say. But again, the discussions from our experience, on the business side, between sales and marketing-- which campaigns do I want to show, do I want to offer to send out in the Sales Cloud? Which interests do we have? Are these interests meaningful? So all these discussions around are, most of the time, the longer part rather than putting things or systems together. And to get this kind of discussion started, Svenja, what helped you most to get an internal buy-in early? Yeah, I think we mentioned that already, but we included from the very, very early beginning, our local marketing teams and our sales colleagues, so from the local markets. And that ensures us that the solution we think could work is their solution. So they buy in. It's directly from the beginning, and they are working as ambassadors. And how did you align KPIs. I mean, you don't like KPIs. I know that, but between marketing and sales? Yeah, as you said, I'm not really on a KPI. I'm working in marketing and with sales, so we love talking. So let's say it's use cases we agreed on and made it happen. And this was only working if you listen to the teams and listen to the market needs. And to bridge between the headquarter marketing strategy and the local market needs. You have to moderate that. What was the biggest mindset change for the teams within that culture? Yeah, you know that if sales does not make their figures, it's always marketing fault. So it was they against us. And with now going the first step into the direction, understanding them, getting into what they really need, what is their concern, we now have the occasion that we feel as one, we feel as we. And we call it one ERCO. Awesome. So as we talked about AI in B2B commerce as well, Jason, what is the easiest possible entry point for AI in B2B commerce without a heavy transformation? So I think what we're seeing in main areas is simplest ways to start by augmenting existing processes rather than replacing them. Common low barrier entry points include the AI-powered search recommendations, so that's improve the product discovery on your existing commerce platforms, using AI-driven personalization. Another area is around chatbots and virtual assistants, so deployment of conversational AI for customer support or order tracking without changing backend systems. Also, we see in the area of demand forecasting and inventory optimization, so using predictive models on existing ERP data to improve the stock planning. And finally, around automated content generation for AI tools, for product descriptions, marketing copy, and translations, which can be easily integrated with current workflows. I think these approaches require minimal infrastructure changes but deliver quick wins. So I think the key message here is be realistic and identify proof of concepts or initiatives that can deliver value quickly. You already mentioned a few, but which of these AI use cases consistently deliver fastest return of investment? Yeah, so the fastest ROI typically comes from efficiency and conversion improvements in the following areas, I would say, around personalized product recommendations. So that's driving higher conversion rates and the average order value up. And it's in other areas around customer service optimization, reducing support costs, but improving response times to your customers. Another area is dynamic pricing, promotions, which is optimizing your margins and competitiveness in real time. And I'll say the final one, and it doesn't really come up on many people's KPIs, is around fraud detection. So that's all about preventing fraud on your e-commerce site, which prevents not just financial loss, but minimizes the operational disruption. These use cases leverage existing data, but have a measurable impact on revenue or even cost reduction. So definitely a lot of things to consider. But I know we love to talk about KPIs. We love to talk about AI capabilities, and agentic AI, and all of that stuff, but what organizational capabilities matter more than technology from your point of view? Yeah, so I think we've touched on theme with Svenja and Christian earlier, but technology is only part of the equation. Success depends upon an organization's readiness. And what do I mean by organizational readiness? It's around data literacy and governance. So teams must understand and trust their data. The data must be clean. It must be accessible, as it's critical to any AI initiative. Change management and culture is another area. So it's about encouraging the adoption and experimentation. Svenja said earlier that they worked with their program, with the marketing and sales working together to deliver a solution that actually works for them. Because in the change management area and the general culture, resistance will kill your ROI on any program. I think the third area is all around cross-functional collaboration. AI projects need input from sales, marketing, operations, IT, whichever area they're going to affect within the business. And it's also about clear business objectives and KPIs. Define your success metrics before implementation to avoid the tech for tech's sake. And it's all about agile decision making. Within any AI program, AI is changing every day. So the ability to iterate quickly based on insights and feedback is absolutely critical. Cool. If I might sum up a little bit, my key takeaways, and hopefully the key takeaways of the audience are get your teams together at the beginning of each project, making sure that you have the right solution in place, that you collect all the information you need before you start, and reach out to the right partner with the right experience, I would say. And make sure you review and remove any data silos and really making the PT members understanding what the benefit is, if I can say it like that. So guys, reach out and leverage Reply's experience. I'm super happy that Svenja could tell us a little bit what her challenges were and how she was successful with that project. And I mean, 90% less questions from sales-- who won't love that? That's awesome. So with that, I would say reach out. We can discuss your challenges. They might be a little bit different than those from ERCO, but I'm 100% sure you will find yourself in those experiences from ERCO as well. And with that, thank you, everyone, for joining us, taking the time. And I hope it was interesting for our audience.