On-demand webinar: Data-driven marketing and business - the supercharger for your business

Annabella Pscherer: Welcome to today's diva-e webinar: Data-driven marketing and business, the supercharger for your business. Today, our experts will show you what it actually means to be data-driven and what impact a data-driven strategy has on your business. I would like to take this opportunity to welcome our speakers Frank and Harald from diva-e and Bruno and Sven from Adverity. Thank you very much for joining us today. I'd like to hand over to our experts and wish you lots of fun and exciting insights from the webinar.

Frank Rauchfuß: Yes, welcome again from my side. If we look at the agenda together, we've brought some exciting topics with us today. On the one hand, a brief introduction: what is actually-, who is actually diva-e, what does Adverity do? And then the really introductory, exciting part: What do I actually need to be able to do data-driven marketing? What are the skills required and the approach? And then actually the main part: we have brought two exciting customer cases with us, where it then becomes relevant in practice and we also show how data-driven marketing best practice is ultimately used. And finally, please feel free to ask questions during the webinar - hence the chat box on the side - which we will be happy to answer. We would like to introduce all four of us again. Starting with Harald. Harald, I think you're still muted.

Harald Butterweck: Yes, of course. Harald Butterweck, I'm an Expert Manager in Consulting at diva-e. I've been working in the field of data-driven business and marketing e-commerce for a little over seven years now and will be presenting some of the cases today. I'm really looking forward to the seminar.

Bruno Kofler: Yes, hello, Bruno Kofler is my name. I'm a sales manager at Adverity. In other words, I'm not responsible for implementation, but really for strategic development, for planning all kinds of data-driven marketing projects. Always together with the customer. And today I will also be presenting a case study from Odlo, one of my favorite clients.

Sven Wöltjen: Yes, then I'll continue right away. My name is Sven Wöltjen, I am Director Partner Management at Adverity. This means that I am ultimately responsible for our partnership, our good partnership with diva-e. I'm looking forward to the webinar today, I'm very much looking forward to the cases that will be presented and I would also be delighted if many participants have lots of questions.

Frank Rauchfuß: Welcome again from my side. I'm Frank Rauchfuß, I'm Managing Director of diva-e Products and I'm responsible for our own products that we offer, as well as partner products. And I'm very pleased that we're organizing this seminar together with Adverity today and that we're able to present some exciting insights.

If we take a quick look at diva-e as a brief introduction and go one page further, diva-e has around 800 experts, 13 locations where we are present and over 20 years of expertise - especially in the digital business. We are the number one e-commerce partner in Germany with a turnover of almost 60 million and what sets us apart is that we combine e-commerce expertise with marketing tech expertise and, in this respect, we bring together the interplay between data and control perfectly and see ourselves as the provider that defines and implements the ideal marketing tech stack for customers in a tool-agnostic and independent manner. And in this respect, we are also among the top five digital service providers with multiple awards and partnerships. Ultimately, our approach is to position and present ourselves as a transactional experience partner. This means that we feel responsible for ensuring that, on the one hand, the customer enjoys a very, very good and interesting and user-friendly experience on our customers' portals, but at the same time that it is also transactional. Because ultimately, transactions are business-enabling and have positive effects - and this ultimately requires various services. Starting with a strategy and consulting, which we offer, to a technology selection, as I just mentioned, and then also bringing in the platforms and digital commerce capabilities and setting up e-commerce portals. And, last but not least, ultimately ensuring performance, i.e. how I bring qualified traffic to these full, built e-commerce portals via marketing automation and, in this respect, actually being a full-service provider that offers the complete value chain in the digital sector, from customer approach and strategy through to implementation and real-time operation.

Sven Wöltjen: Yes, I'll go on and briefly introduce Adverity. We are one of diva-e's technology partners. We are a relatively young company, founded just over five years ago. But in the meantime, and this is typical for start-ups, we have 170 employees - that's probably no longer true. There are probably a few more of us. We were founded by three media and marketing experts - that's very important. Because they simply recognized the need that ultimately prevails in marketing. And we will see in a moment, especially in the case, what the solution approach is. We're from Vienna, you'll hear that from Bruno in a moment. He is Viennese. We have other colleagues and other offices in London and New York - so we are on the move globally. I don't think anyone has ever seen the New York office - that's a little anecdote about Covid - no one has ever set foot inside. Nobody has ever been there, simply because it's closed. I myself am also based in Hamburg, haven't seen my colleagues in Vienna and London for a long time, but I hope to be able to catch up again soon and hope that we can get it all done soon. Yes, what we are doing - and this is the one sentence that we are just saying - is that we are enabling marketing employees, marketing units, to simply understand what their contribution is, but also what - how they can ultimately maximize it. Ultimately, to be able to increase the success of the company. And you can see how we do this on the next slide. Let's take a quick look at that. What we have here is a platform that ultimately makes it possible to connect a great many - it says over 400 here, there are probably even more - marketing sales, CRM resources, ultimately any system can be connected to our platform. What we then do with these connectors that we already have is that we harmonize the data that we collect.

In many systems, for example Google, Facebook, Instagram, there are very similar data formats, but they are sometimes named differently or the date format is sometimes different. Sometimes there are also spelling mistakes. Ultimately, we harmonize the data and can also enrich it with additional data from other systems. And then pass it on to a BI system for further analysis, for example, but also have our own systems that ultimately create dashboards and are also able to use machine learning to suggest something like a marketing mix, i.e. make suggestions: What would be an optimization opportunity in marketing. In other words, we collect the data, process it and ultimately provide further insights, dashboards and also enable initial suggestions via machine learning. We are very independent here and, as I said, we can use our own tool to carry out further analyses. However, we can also ultimately forward the data as a feed to other BI systems such as Click, SAP or Tableau, for example, so that the infrastructure that is already in place can continue to be used. So, here's a quick overview, but I think it's important to understand a little bit about what we do. And I think we'll see later in Bruno's case how we ultimately implement this. And of course we will also be available to answer any further questions.

Frank Rauchfuß: If you look at the combined, joint customer portfolio, there are various industries that ultimately have a need to promote and operate data-driven business. And that means you can see all sectors, all verticals from a sports club to a furniture department store. Everything is possible in the fashion sector. In other words, data is a very important topic across all industries and is already being implemented by various companies. If you look at what actually brings Adverity and diva-e together, it's the following on the next page: That we bring together implementation know-how, marketing know-how, e-commerce and platform know-how with a leading customer data platform and, depending on the customer's requirements, customer insights and better business decisions can be made accordingly. This is also the key success of a customer data platform. So, now let's get into the content - enough with the introductions.

And, yes, we actually start with the topic: What is the business approach? What challenges do you face? And how can you overcome them? And if you look at the volumes of data that are coming at you today, especially as a company, it's a huge variety of different channels, a huge amount of data. And at the end of the day, if-. One learning, a productive learning from Covid, is that it is precisely this data that has helped companies to react to the changing usage behavior of customers and ultimately this is the currency: having personal data under data protection aspects that make it possible to make customer-specific offers, but also to make appropriate budget calculations in a meaningful way. And on the other hand, of course, you are faced with the challenges that you see on the next page. Ultimately, you no longer have different data sources, but you also have the issue: Which money do I actually put into which channel? So that means that I track marketing budgets, that I understand what ROI I actually have? And which channel works particularly well? What is the customer journey? And making the right decisions about it. On the other hand, I have the challenge of ultimately looking at a technology landscape with over 7,000 tools that are growing rather than shrinking and consolidating. Where I have to understand, for myself and my business model, which logical functions fit together best, but also enable the organization to use them to get the maximum benefit.

And of course, this requires a great deal of complexity to be mastered in order to ultimately convert the opportunities or advantages that I gain from addressing customers. And last but not least, there is the issue of data privacy, the GDPR - what needs to be answered and supported. Ultimately, these are the main challenges that you have to deal with at the start of a project or situation like this. But of course they can all be solved. And if you solve them, they have a very, very big positive impact on the organization, but also on the business. If we start with the customer journey, I would like to hand over to Harald, who explains what an approach can be.

Harald Butterweck: Exactly. Let's take a deeper look at this topic once again. Let's take a look at the actual points at which data is ultimately generated, where it is processed and where decisions are made. And here we have presented a customer journey that ultimately occurs in many, many companies. We have the approach, i.e. the approach to the customer. We ultimately have the over-reading phase, where the customer considers whether they should buy, whether they should ultimately use the service, and so on. We then have this real purchase aspect that comes in here. Then we ultimately have a win-back process to bring the customer back, to ultimately speak to them again. And then there is the final stage, so to speak, loyalty. This means that the customer is ultimately also able to connect with the brand, that they ultimately also live this brand to a certain extent. There aren't many, many, many points on this slide. Let's take a look at a very, very few. We have a webinar today, which is a PR campaign at the end of the day, which we're looking at right at the front.

Ultimately, there's the approach - we're doing it here right now, together with Adverity and diva-e. There are still steps to be taken along the way until the final decision is made: We look at the webinar, there is a landing page, an e-mail that the webinar will ultimately take place. We're now in the webinar, which means we've left consideration behind us, we're in purchasing, so to speak. I've bought in emotionally - I'm watching the webinar. I'm on the website, I'm in the meeting. I'm looking at this website, I now have direct interaction with the four of us sitting here. I have the opportunity to chat directly in this community, ask questions and so on. I can also ask questions afterwards, all four of us are absolutely open to answering questions again. And then it really goes in this direction, which will come in the future, is loyalty. It's about us sending out another e-mail where the seminar can be downloaded again. And so we have mapped this entire journey. If you now collect all this data, this is a very, very large part of this seminar alone.

And in order to actually be able to use it, we screen companies. That brings us to the next slide. This is a so-called CDP that can connect all these data, these data points that we have just seen. Here at the front in the data area on the left-hand side, the personal data, the engagement. Logging in to the go-to-meeting and possibly downloading the webinar again afterwards. All of this data comes together, is summarized and is then entered into a CDP, which is what we are talking about today. The CDP ultimately closes this circle. It harmonizes, as we heard from Sven earlier. It harmonizes, it brings together. It also includes segmentation. In other words, which, ultimately, participants do we have today? Sure, we have some from our own company - that's a segment group. External or ultimately you, as you are listening right now, is a group that can certainly be subdivided again.

And then there's predictive. In other words, these are the predictions, these predictive ultimately further steps that could be recommended. And then we come out of the CDP into an orchestration and there we come to the next steps, for example the email that you receive afterwards. And at the very end, there is the customer. In this case, that includes everyone who is currently listening. The reason why diva-e is ultimately very, very strongly connected in this CDP area and now with Adverity, and is ultimately also a partner that you can rely on, is that we bring these three major points together under a CDP or marketing text in general.

On the one hand, we have data business expertise, where I have my absolute hobbyhorse with years of ultimately looking at data-driven business consulting, creating use cases and looking at them: What do we really need? Where do we also have a direct edit value for the customer? And ultimately this whole conceptual part, which then also goes into sales planning and implementation, where I am always relatively strongly integrated with the software expertise that we then bring in. Today, for example, Adverity, who are ultimately the absolute experts in providing this harmonization, this connection and this platform.

And then there is our third point, which is operational expertise. We have our own data centers, we have experts in the cloud and, ultimately, we also have the operations for a number of web stores and platforms that we ultimately support and provide customer service for. And with these three major points together, that actually means you're in pretty good hands. Now to go a little deeper again, where we have now returned to this higher level of fluidity. How do we get to the point where we ultimately have a CDP? That's our standard approach. Very module-driven - in other words, every single point is more or less a module. It always depends on where the customer is at the moment, where you come in. And then the whole thing is adapted to the customer's needs. If you look at the end-to-end, I come out of an analysis, have a use case development, also with requirements - where do I ultimately want to start? Then, of course, the evaluation: What tools do I need? What else do I perhaps need behind the orchestration, what else needs to come out of it? Which tools do I also want to address? In the middle here, for example, is Adverity, which ultimately does the amortization, as a CDP provider, in order to ultimately get this view: Is the whole thing worthwhile? Do I have a business case? What is the return on investment? What do I have to do to be able to implement it? And then you start to implement an initial MVP.

The implementation is planned, all the prerequisites are brought in, firewalls are looked at, data centers are looked at and then the first MVP is built. Ultimately, the first MVP is just the first step into the new digital world of CDP. In other words, this first platform, which is here once, is then challenged again and expanded further. And then there are the last two further points, which will gradually alternate. In other words, an extension, a new business case, which is now also step-. A use case that is added from step two, which is now also implemented, is then ultimately developed influentially. The integration in Adverity, where I am a big fan of Adverity, is incredibly fast. I have a huge pool of connectors that I can use directly. But I also have the option of building a custom connector, which is a great advantage for someone who works at diva-e, where I have many, many, many colleagues who do this on a daily basis. The whole thing is then developed further in exactly the same way.

And that brings me to our customer case, our first one. Then I'll hand over to the second and to Bruno. Our first is ultimately medi GmbH. Medi, who ultimately make a lot of supports, compression stockings et cetera, prostheses et cetera. A super cool company. It's represented in 25 countries worldwide, has over 3,000 employees and has three divisions, which we'll come back to in a moment. Let's take a quick look at how the whole thing is structured, which I will briefly introduce. Let's start with: Where are we today? Where is the situation in which we started? What was our approach that we started with and what did we do? And then afterwards - what is the edit value that ultimately came out of it? And here too, when we get to it in a moment, a little outlook: Where will we go from here? First of all, the starting point: Where do we come from? We have a, partly medical manufacturer, medical products. A manufacturer of medical products. It is also very important to look at where they are sold, how they are sold. This black-and-white listing is about: there are lean concepts that ultimately not every product may be sold everywhere, or if it is sold as a medical product in a certain country, for example, it may not also be sold here as a B2B product. Important: We do not have direct sales here. We are a B2B. In a B2B case, it's also about consolidating the market position in B2C and creating brand awareness. And at the very end, it's also about looking at the end of the day: What price do I have? What is the average market price? And where do I want to develop in the future? The situation at Medi is -. Ultimately, I have three major divisions. One is Medi, which is so heavily involved in the medical sector. Then I have CEP, which is more sports equipment. And then I have ITEM m6 - that's ultimately the lifestyle sector. In other words, these three divisions all have their own customer journey, their own starting points, if we imagine the customer journey again.

Important: Always do the whole thing with a retailer, i.e. with a B2B customer at the end. We're not going to talk about a B2C customer here, Bruno will go into that in a moment. And that brings us to this: What did we do? How did we approach the whole thing? Data quality is very, very important, especially in an environment like this, where I ultimately have multi-dimensional data, including very different data from very different sources. This means always keeping an eye on data quality. Here, Adverity is a partner that can absolutely hold the whole thing together and also holds it together in a reliable long-term manner. Which is a big difference to “I build a small API myself”. Harmonization is a very large part of the process, which ultimately took place at Adverity for us.

Ultimately, the agile approach is our approach, where we say: “We are gradually expanding the dashboards together with you, with the customer in this case”, so that we have ultimately always gained a deeper and deeper understanding of what the customer wants to see in the end. In this case, the customer is an internal stakeholder who ultimately makes the decisions and then takes further action. Technology is always a big factor. It has to be reliable, as we have already seen in data quality and harmonization - and this is where we ultimately get a little deeper. Let me take you on a journey through the architecture here. Here we can see that we have received project lists or, in some cases, product data from Medi's own environment. These were then ultimately transferred to the data lake. And then we have this exciting part: CDP, which ultimately combines many data sources. And connecting a data source is ultimately much easier compared to a few years ago, when I was actually still doing it by hand - I built my first CDP when I didn't even know that CDPs existed. And with hindsight, I can now say: “I built the CDP by hand. With the ETL route.”

But at the end of the day, it's all about connecting advanced pricing data, for example, which was ultimately provided to us by an expert company or which we ultimately extracted here. This was also harmonized together with social media and user tracking data. Ultimately, everything from the website was combined into one stream and then adapted here in the data warehouse. The data from Medi was then added internally to the data warehouse and the whole thing then became an extended data model and what ultimately came out of the project is: I once had reliable reporting. Everyone knows that they can customize their reports because there is another BI behind it. I now have the opportunity to make decisions based on data, on data that is ultimately also very, very up-to-date because it is connected live. We're not talking about real time, we're talking about actually having the data at the speed we need it in this case.

And, where we now have a major advantage, we ultimately have the opportunity to connect our B2B customers directly. We'll get to that in the next part, namely in the edit value that we have. We have price stability through tracking, through reacting, through responding on-point to different actions in different countries. As a B2B company, I then have the opportunity to keep my prices more stable, even right up to the end customer. I can control my brand awareness quite deliberately and ultimately also counteract it to some extent and sometimes take part in a nice campaign that I can also track completely. I have the opportunity to respond precisely to my B2B retailer because I know the country in which they sell. Because I ultimately know what situation they are in thanks to the transparency I have in the next point. And I can respond to them specifically here. The next point, where we go into the future again, would be dynamic pricing, monitoring, which already exists today. But dynamic pricing would be another step towards taking the whole thing to a new level. And so we have now completed a case that has taken quite a while and is very, very exciting. The key takeaways are definitely this price stability and brand awareness and ultimately also being able to respond to the custom, i.e. my B2B customer, and ultimately also being able to work with them. And with that, I'll hand over to Bruno.

Bruno Kofler: Yes, let's take a look at another case study from Adverity. Well, you've already seen that, especially with the logos. So, we are in a wide variety of countries, sectors and industries. But I have now chosen Odlo because we simply do a lot in the retail e-commerce sector and because, as I said, Odlo is simply-. I'm a very big fan of the company. So, if anyone doesn't know Odlo, it's a sports company based in Switzerland that makes very high-quality performance wear, outdoor, all kinds of sportswear. Basically-. Odlo has a very long history behind it, so they've been on the market for over 50 years and are therefore a very classic retailer. That's how the whole thing started.

The products are now available in over 8,000 physical stores worldwide. Then, as you can see in point two, they now also have a very large online store, where a lot of sales are also generated via this online store. And, as I said, the whole thing is focused on Europe, but is now also very strong in the American market, for example. In other words, the territories and countries are now very broadly diversified. And now this combination of retail and e-commerce on a truly global level - as you can see in point four - is leading to a very large mountain of marketing, sales and e-commerce data. All sorts of things. In other words, in this case too, we had the usual candidates in the area of which channels are active. So, of course, all kinds of social media, Google Analytics, Google Ads - in other words, the entire Google suite. Of course, the e-shop is extremely important. All other possible tools and areas, including Excel Shields. And that's very, very important, because that's also how some of the data from retailers arrives. In addition to the physical stores where they offer the products, they also have a large number of concept stores that they operate themselves. So it really is a very broadly diversified network, which simply leads to an extremely large amount of data. And the problem with the situation was that the data infrastructure was very limited, or even non-existent. This meant that all the reporting and analysis took place in the individual tools themselves.

In the specific case, for example, there was a marketeer who looked at Facebook, for example, logged into Facebook, looked at the data there manually and tried to find out: Okay, what works well? What works badly? Where can we optimize further? Then, when data was needed for reporting, this person had to download the data, somehow put it together manually, insert some of it back into Excel sheets, somehow harmonize everything, bring it halfway to a common denominator. In other words, a very complex system. Of course, the same had to be done for Google Ads and every other tool. As you can see, there was really a lack of a single place where all the data came together and was available. So that was the big problem. And also-. The harmonization that we are doing has already been mentioned a bit. If you now also consider that you operate in different countries with different date formats and different currencies, then the whole thing becomes even more complicated.

And then on the next slide you can see a bit like this: What are the challenges that have arisen from this situation? Well, the biggest challenge at Odlo was simply not having a combined overview of all the data. This makes it very difficult to compare marketing campaigns with each other. That was a real problem. So, the evaluation of the conversion, of the individual campaigns, was extremely difficult. So you can see that in point one: there were a lot of silos. That's what I was talking about: I have my data in Facebook, in Google Ads, in all kinds of platforms, but nowhere is it really consistent and clear. And as a result, getting, processing and analyzing data is extremely complex and extremely time-consuming, as described. So, that also leads to a lack of control over the budget, over the spend, especially on a daily level.

So, allocating the budget to the activities with the highest output, with the highest return, was extremely difficult in this case because it was so difficult to really find out online: Okay, what are the activities, what are the campaigns with the highest return? So that is of course the first question I have to answer here. And that made it very difficult to find the right marketing mix in point five. In other words, it was difficult to allocate and control the budget - especially in the short term. Recognizing short-term trends and taking a short-term strategic approach was virtually impossible in marketing. Because I simply had several countries, several currencies, several campaigns, an extremely large amount of data. And it took me a very long time to really process this data. So it was really a problem to find out: Okay, what is the current situation and where do I need to look in order to optimize? In other words, on the next slide you can see the problem of control again.

Without a place where all the data is collected and available, something is simply missing. And Odlo had too much data here, yes, which is generally not a bad thing. But it was simply not possible for them to collect and view this data on a daily basis. And as a result, I lacked control over the marketing budget, over the marketing budget. And it was really difficult to track the most important KPIs alone. Yes, one big example was the relationship between marketing spend and sales in the store. That was simply an example - it was very obvious that you really need an overview and transparency here. But that was simply missing because it was also very difficult to combine this marketing and e-commerce data. And another big problem was that there was too much data, so the time required for day-to-day operations was not really worth it. So, it wasn't possible to optimize because I couldn't get and process and view and understand all the data so quickly. And that's also a bit of a paradoxical situation that we actually see very often. Because basically, the goal of every marketer is to really improve KPIs and performance. But at the same time, the problem is often that there is simply not enough data and, above all, not enough time to process this data so that the whole thing can be implemented and optimized effectively. That's the big problem. And we can see on the next slide that this simply means that I can't find the right marketing mix. But fundamentally, of course, it is extremely important that I really understand the budget and that I can control the budget. In other words, finding the right marketing mix. And the question here was simply: how and where do I best invest the budget?

And, as I said, Odlo had simply only stored the data in the individual platforms, not in one place, which meant that it was simply not possible to create a clear, holistic picture in marketing. And then to really do this cross-channel, yes, as I said, to link marketing and sales data and then find the right balance, the right activities. Yes, and we really helped them with that. We helped them to harmonize all the data, to bring it into one system and harmonize it there. As I said, to the same currency, to the same date, to the same naming conventions. And so that we can then really use the data to really understand the ROI of the campaigns for the first time. And then to really optimize them. In other words: What did we really do in concrete terms? Let's take a look at the next slide. So, if you can optimize the campaigns in the best possible way, as mentioned before, then you can improve the KPIs and really be one step ahead of the competition. In other words, that is always our goal. Odlo is generally a very innovative company when it comes to products. So they always have very new, very innovative technical materials, for example sustainable products, etc. But in the area of data integration, in the area of marketing reporting, they were not up to date. That was rather new territory for them. And we basically took over this area and helped them to stay ahead of the competition in a very competitive market and to work quickly and agilely.

In other words, in point one, what have we just done: our core competence. We took a look: Okay, where is the data everywhere? We then really connected all possible data sources and brought them into one platform. That means I really have one place where everything is available. Very transparent for all possible stakeholders. And then we worked a lot with harmonization, with different transformation mapping, for example, to bring everything from Swiss francs and euros and dollars and other things down to a common denominator. Likewise with the naming conventions of all the different platforms. In order to really bring everything down to a common denominator, so that I can really compare all the data, we have really automated all the manual work and thus really taken the whole reporting landscape to a new level. That was really our big goal. And through dashboards, where there is really consistent data, where there is correct data. In other words, we also have various systems - we have various machine learning systems that constantly read the data and then say, for example: “Hey, the data quality is not right here, an error has occurred here, a data field has not been filled in here.” In other words, we also work a lot on this. And, as I said, we have created dashboards where everything is up to date, where everything is consistent, where everything is error-free.

And with these dashboards, you can really understand things better: Okay, where is my budget going? What is the return? And that makes it much easier to manage the budget. It's much more efficient, but also more effective. You can significantly increase the output. In other words, that's what we've really achieved here: getting the highest possible return from marketing. Yes, and really based on the entire funnel. In other words, data-driven decisions, as in point three, were made along the entire purchasing funnel. In other words, it was always understandable: Okay, what works in which part of the funnel? And how do my customers act? What is best received? This also means that all the touch points in the funnel have become more personalized and better adapted. And, as I mentioned earlier, we were able to make Odlo much more agile and much faster. And this has enabled them to react better to customers, trends and competitors.

And now, perhaps on my last slide, I'd like to briefly ask: What was the real result of the entire project? Well, really broken down into figures. Basically, we made sure that we created a standardized, efficient reporting and performance monitoring system together. We also ensured a great deal of transparency here. That was also an important point here. That there are no individual people hoarding certain data in their silos, so to speak. Instead, it really is extremely understandable and accessible for management: Okay, where is what happening? And you can also create reports more quickly at short notice. If you look at point one: Saving time. That really is -. For us, increasing efficiency is always one of the most important points. In other words, we simply want to free up time for the marketeers through our automation. So that they can then use the time better to optimize further. That's one of our approaches. As a marketeer, I don't just want to constantly move data from A to B and somehow not play around with the much-loved Excel formulas. Instead, I really want to continue to optimize, improve campaigns and achieve more. And that's really what we want to make possible.

In other words, in this case we were able to reduce the number of quasi-processes, i.e. the time required for all these data processes. I hope we can reduce that even further. Because if we look at other customers, now at Ikea for example, we have really been able to make this process 100 percent more effective. In other words, we really have completely automated the entire process. And it's the same with most customers, they really say: Okay, they've become 90, 95 percent more efficient. So it really is an extreme time saving. So, for example, we now also have a customer, Cars.com. It's an online e-commerce marketplace. They also said they spent 160 hours a week just collecting and standardizing data. And together, we were able to reduce this again and again and automate it even more. And in the end, we ended up with five hours per week.

That is, if you really look at it: Five hours a week, that's one person doing a bit of this on the side. 160 hours a week, I basically need a whole team to deal with it. So, saving time is really one of our most important points. Then point two is data quality, which we have been able to increase significantly. That is also very understandable - if I do the whole process manually, errors and inaccuracies simply happen from time to time. In other words, there are simply errors that creep in here. As I said, data fields that are not filled in or data that is not consistent, that I have currency differences or naming conventions that somehow shift. And, as I said, we have a system here that recognizes the errors, which then issues alerts with: “Hey, something hasn't been filled in here”, “Something is in the wrong column here”. And then you can quickly improve the whole thing. Because basically, if I do optimization but it's based on incorrect data, that's not a desirable scenario. In other words, it is always important for us to look at the data quality and improve it. And then we also look at our day-to-day business. This means that we have also been able to achieve significant improvements here, together with Odlo. Above all, the decision-making process is much faster and this simply makes me more agile and flexible. As I already mentioned, I can react very quickly to trends, customers and competitors. And optimize my campaigns and activities in a really targeted way. In other words, we basically manage to optimize 15 to 20 percent of our marketing expenditure, which can then be reinvested. This means that I can organize my budget better, control it better and achieve better results.

In other words, I naturally want to increase turnover, growth and profit, and in this and many other cases we have succeeded very well together. And, yes. If someone in the audience now says: “Okay, that's for us. It definitely sounds exciting, could be interesting for us too", then we now have a kind of special. So, we have developed the option of a free trial for webinar participants. What does that mean? Well, we are now very happy to simply sit down together, have a chat and take a look: Where do we currently stand in the reporting area? How do you currently evaluate marketing data and what potential is there? What are perhaps difficulties that can be improved? And then we can say: Okay, you make the software available for 30 days and look at two or three data sources that you link, for example. And there is also full support, full onboarding. Then we take a look - you can really find out: How does the data come in? How is the data processed? And then also the first dashboards - so how can I visualize this data with us? As I said, you can see the benefits here again: the manual work is extremely reduced, data quality is increased, the risk of errors is reduced. I have a better overview, more transparency and can then, as I said, achieve the big goal: I can optimize better and thus really increase my ROI. So, as I said, if anyone is interested, please get in touch. Then we'll be happy to look at it together and can also provide a free per se. Yes, then we're actually already at the Q and A. So, if there are any questions now or if questions have already been asked, please feel free to contact us at any time.