Speaker 0: Hello, everybody. If you’re listening and you can hear me, give us a thumbs up in the chat. Let us know where you’re from, where you’re joining us from today for MaDreaming. Hopefully, everybody can hear me loud and clear. Welcome to MaDreaming. Um, I’m so excited for this. This is the first day of MaDreaming and we’re really gonna kick things off, um, with our two presenters right now. My name’s Laura Curtis. I’m from Sercante and I’m gonna be moderating your session today. Now before we get started, I do have a few housekeeping items to cover. So number one on the top of everybody’s mind, yes, this session will be recorded and will be available on demand after the event. We’ll also be following up with an email. If you have any questions, our speakers here are here to answer them. So post them under the Q and a tab. And lastly, make sure you use that chat. I’m not seeing any messages come through, so I’m hoping that I’m not talking to an empty room. Hopefully everybody’s eager to start, but make sure you use that chat there’s emojis gifts and a lot more. We want to hear from you. Now let’s get started. I’d like to introduce you to ECAT and Cyril who have an awesome session ready for us today, which is datas to dollars, transforming the financial sector with data cloud. Take it away.

Speaker 1: Hello, everyone, and welcome to our session, data to dollars. At, um, here you will, uh, learn how we can transform, uh, financial sector clients with the help of Salesforce data cloud. And together with Cyril, we will guide you through the journey. Welcome.

Speaker 2: So first of all, before we start, I would like to also to, uh, to give a a kudos to, uh, the incredible sponsors that we have today. Um, and thanks to them that we have the opportunity to have this awesome, uh, a community, uh, conference. And, uh, also, I would like to thank you for attending that conference and, uh, for having, uh, chosen to to join our session. Thank you very much. Um, the agenda of that session, um, so we will, um, start with listing some of this the the the the main marketers’ struggles, and we will see how we can now identify solution in two different paths to answer those those challenge. Um, we will start with the vision of data cloud specifically for marketers, so how we can use that. And then we will focus on Data Cloud for financial institution institutions. So we will see what are currently the main marketing challenges for financial services, and we will dig into specific use cases and see how Data Cloud will specifically help us to to solve them.

Speaker 1: But first, uh, let us, uh, focus on what are the main struggles of marketeers. So you know that, um, for marketing strategy, we are using multiple system to achieve what we want. And, basically, marketers struggle with the lack of unified customer view since once we connect once we have all data sources available, it’s really become becoming hard to connect everything at one place and have a full view and full picture on the customer profile. And that leads to another struggle that marketers might have, which is, uh, inability to personalize recommendation and offers for the customers effectively. So, basically, if you don’t have the full picture about your customers, then it becomes hard to understand their intent, their preferences, and their needs, and then respond to them with, uh, relevant marketing activities and, uh, of course, messaging. And, uh, this leads us to another difficulty, which is, uh, making data driven decisions. So lack of data, which do bad decisions, uh, and, uh, we are not able to personalize well or target relevant customer segments, and this becomes, uh, really hard to then reflect in business needs like, uh, decrease in, uh, purchases or revenues and other metrics related to marketing. There are other challenges in, uh, measuring the marketing, uh, return on investment. Because if you have a lack of data and customer profile and, uh, then you don’t know, um, how you’re performing, if you’re doing good with your marketing activities or if you need to improve, uh, improve them. So these, uh, in our perspective are the main challenges that marketers face, uh, nowadays.

Speaker 2: And, uh, we also wanted to to share with you one number because we we all love, uh, numbers, um, about this inefficient targeting in marketing actions. So we just saw with, um, Ecast that marketers are struggling with unified customer view, with personalization, and indeed, 78% of marketing campaigns are said to be not effectively targeted, mainly due to incomplete customer profiles. So what we’ll do, let’s see what kind of approach could could solve the the this problem. What what what can we do to start to, um, pass by the the discrimination?

Speaker 1: Basically, we offer and based on our experience, uh, we focus on two part solution. Uh, as I mentioned, data everything is starting with data. Data is, uh, core. And, uh, with the one part solution as data, we are focusing on unifying and optimizing customer data, preparing them to be more accurate, accessible, and actionable. So this is, uh, a solid basis for, uh, the other part that we focus on, uh, for the rest of the solution.

Speaker 2: Mhmm. Yeah. And and now that we have data and we know how to merge the the the the data around a unified customer view, and we can leverage also the data to personalize the campaign. So this is truly how to act and make those data actionable to the marketing. So the two part solution data plus, marketing. So now let’s dig into data cloud specifically and see how that specific solution will help marketers.

Speaker 1: I would also mention that, um, having, uh, data available and well prepared, uh, is not definitely enough. Uh, so that’s why, uh, we divided the solution into two parts, data and marketing. With the marketing part, we actually act on this prepared data. And probably some of you know that there is a golden rule, uh, called GIGO, garbage in, garbage out, meaning that even marketing, you can you can do marketing perfectly. But if you have lack of data, if you have incomplete profiles or you’re not targeting, uh, right segments, then, of course, it doesn’t bring you the the relevant results. And Suriela is right that, uh, we have the technical solution covering both of the parts, which is data cloud. We all know that, uh, data cloud is presented on the market by Salesforce for three main aspects. One is Data Cloud for data, uh, then we have Data Cloud for marketing, and then Data Cloud for insights. So, specifically, today, we will focus on how data cloud product helps marketers to achieve, um, great results as well as, uh, solve their challenges that we mentioned in the beginning of our presentation. So I will guide you through some specific areas of data cloud, or how we call it data cloud journey, uh, because, of course, it’s divided in several steps to achieve what you want. And first area is focused on data harmonization. As I mentioned, there is definitely a lot of data sources that you need to connect, And Data Cloud helps you to connect all data together so that you complete customer profile at one place, and it helps you to know your customers better. With Data Cloud, you are not focusing only on marketing data, but you can go beyond and add and act on other, uh, data sources that could be relevant for your segmentation efforts and, uh, also your, um, marketing, uh, effort overall. Uh, with data cloud, you’re also building your unified customer profiles because the system, as it was called before CDP, has the CDP capabilities to, uh, relate to different identification, uh, um, attributes and complete profiles, uh, to, uh, achieve one to one personalization with marketing effort. So after, uh, data is harmonized and all sources are brought in, uh, we focus on data segmentation because well prepared data helps us to segment better. And with the help of predictive intelligence, uh, of data cloud, we could discover new segments which could be used for, uh, marketing efforts, uh, further on. Uh, so our segment become, um, more precise. And, uh, we use, uh, Data Cloud for as a single place for, uh, the segmentation activity overall. Data Cloud also has a feature called calculated insights. So this is, again, the part of artificial intelligence that will help marketeers and support them in their daily activities to understand data better and, uh, of course, reflect in, uh, marketing activities. But as I mentioned, having data prepared, uh, um, and actionable and segmented is not enough. We need to act upon this. I I need to mention that data cloud is not responsible for the actual activation of marketing efforts. So data cloud is rather used for preparing everything to act on the data in other platforms like Martin Cloud engagement. So with data activation, I’d rather mean that, yeah, we are focusing on empowering one to one personalization that can be used then through marketing activation platforms. Uh, we are avoiding irrelevant messages because we simply know that, uh, our profiles are unified and we are targeting the right segments. It drive, uh, it drives customer engagement for sure, and we can, uh, target and scale upon that. And, um, that’s, again, not enough because we need to know how we’re performing, if we are doing well or not, if we need to improve something. So Data Cloud has these capabilities to give us data insights. And here, again, artificial intelligence come, uh, comes into the game. So it helps us to explore our data better and bring us some customer insights. So who are our customers, whom we should target more, who whom should we contact less, for instance, because they’re overcommunicated. It has the power of analytics and, uh, gives us a chance over the time.

Speaker 2: And all those different areas that we can work on with that cloud will also bring you a clear value. So the first one, obviously, when you will get different information and knowledge from different data sources will be to build that single source of truth. Um, you will have actions like additive resolutions to, um, get a clear and complete view of your customer coming from all the different sources. With that information and, um, accessible, you will now be able to build meaningful segments and more detailed criteria to group individuals, uh, and make your communication more, uh, impactful. Uh, another another impact, uh, of data at the vision will be also, obviously, to personalize, uh, your your communication. You have more data as you’re able you you will be able to more, uh, customize your personal your your communication. And the more communication that you do, the more data you have, uh, output. And you can also analyze those data to fine tune, uh, your segments. So you can use data in and data out, and, uh, and the whole stack will help you to know better your customer and to, uh, personalize your communications.

Speaker 1: Okay. Um, I want to mention also that, uh, as you might observe that we focus with data cloud on several areas. Uh, but overall, uh, data cloud is not a thing. Uh, it is basically a set of capabilities. So here you can see what capabilities, uh, data cloud has. And, basically, it’s up on you on or your client to which capabilities you will use and leverage, uh, with the tool. As, uh, it doesn’t mean that you need to focus on each of it and cover every single area, but it’s rather take the most out of all available capabilities and use them for achieving, uh, good results, uh, with the marketing activation campaigns. And let’s now focus on the architecture. So we have prepared for you, um, I would say rather simplify the architecture, how we see it, and how data cloud fits in the Salesforce current technical stack. So you can see the data cloud is rather, um, focuses on com combining internal, uh, and external data so that we can, uh, connect the sources. So for a financial industry specifically, it means that we connect data like data about our accounts, transactions, as well as credit score, uh, maybe some risk data, etcetera, that could we could bring in into data cloud and then build, uh, richer profiles, unified profiles of the customers, uh, for both unknown and known audiences. Because we know that, uh, if we are focused on, let’s say, some web activities where people could not be identified specifically until they, as it’s called, raising their digital hand or, uh, activating and identifying themselves, So we still, uh, track them as rather unknown profiles. So this is also the possibility of data cloud to, uh, use such kind of audience until the, um, the segment or this group is identified. But there are other clouds that help, uh, data cloud to use, uh, the power of marketing activation. And here it goes, uh, the marketing cloud engagement tool, uh, where the core product is journey builder that is responsible for orchestrating, uh, your owned and paid channels to create optimal customer journey paths. So the actual marketing activation basically, uh, comes, uh, inside of the marketing cloud engagement environment. But we have also the help of marketing cloud personalization, where we take the extra layer of personalization and use the real time, um, suggestions for next best offer or actions, product recommendations, uh, for the other activation channels like, uh, website, back to email campaigns, or mobile application, chat. Uh, for financial industry, it could be also ATM. And, um, with the help of, uh, marketing cloud intelligence, so we also use, uh, our, uh, unified data and marketing effort, uh, from the marketing cloud activities, uh, to measure, uh, return on investment and attribution across all the channels that we are acting on. And we also added Einstein part, as you know, that, uh, it suggests and gives us predictive and descriptive analytics to know better how we are performing, uh, overall. So Einstein is rather a supporter of, uh, marketeers’ daily challenges to see, okay, if we have missed something or we need to focus on something, uh, with an extra effort. So Einstein helps us on a daily basis with these features.

Speaker 2: And, um, before to see the features and how do we we can use those cap those capabilities for, uh, resolving some of our challenge, we wanted to share best practice in terms of, um, data journey, I would say, from the data connection to the data activation. And good things to to highlight here is that part of the journey will be outside data cloud itself. So first, you would have to, um, start with data analysis to identify where are your data, uh, identify the different sources that you have or, um, uh, where those data could be needed, uh, are in order to to to to get them in the system. Then you will have the data preparation because value systems means values data model, data formats. So, of course, you will have to anticipate that transformation and identify where could be the gaps. And then you will have the data source definition to me. In that specific case, you everything has been prepared, and you are ready to restart the the data ingestion. So then DataCloud comes in, um, that you have data ingestion. So now the flux the flow of data will will will feed data cloud. And in order to be harmonized, you will work in the data model and data transformation. Then you’re gonna have all your different criteria to start a segmentation. And from that segmentation, you will have the the the part of your your, uh, customers that you can, uh, communicate with, and you will have your segment and so you can activate those data. And at the end, as we said, um, the more communication and more data, so you can also have insights and use also that insights as a new criteria. So let’s dig, uh, into a more specific for each of those steps.

Speaker 1: So as, uh, Tyril mentioned, the data cloud steps in from the data ingestion part. So I will cover the part where data cloud is focused on for, uh, the, uh, for bringing some data solutions. So with data ingestions, we actually leverage the, uh, ability of data cloud to bring in and ingest data security from different sources. We use the power of data streams for that. And you can see that for financial industry, we can bring in some native data sources like financial services cloud for marketing activities, marketing cloud engagement. Web analytics is used for controlling some website activities, as well as if the financial institution is using a mobile application, we can bring in some insights out of that tool, as well as we can imagine that banks and other financial institution could use some transactional systems. So any kind of data that’s relevant for marketing activities and for uh, enhancement of, uh, the segmentation could be brought into, uh, data cloud. There is also, um, a secure level of, uh, using, let’s say, um, new soft integration layer where you can bring different third party sources securely and fast. Uh, data ingestion is focused on, uh, bringing data either in batches or, um, making real streams to get data in real time. And that’s one of the core powers of Data Cloud. After we have our data ingested, we are focused on data, uh, model harmonization, where we actually take, uh, data sources, um, uh, with all available attributes and connect it to the data cloud, so data model objects. And we call it connection of the data lake objects to, uh, data model object that are available on, um, data cloud side. So as you might know that, uh, yes, Data Cloud has its own model objects, but we, uh, can, of course, bring some, uh, customized objects if, uh, this native object are not enough. But it might look very easy for you to just connect one attribute to another. But, uh, the challenge is to know what to connect with what what. So that is the part of the data preparation part where you actually build some sort of data dictionary to know that, uh, how, uh, brought data sources into data cloud so you connect with available, uh, data model objects. That’s how you can, uh, help yourself, uh, before the actual data harmonization part. Uh, on this slide, I want to highlight two parts, basically, which is data segmentation and activation because they’re interrelated. Uh, data clouds is leveraged to create segments based on those, uh, uh, customer profiles and activities like event scores that we can calculate with the help of predictive intelligence as well as behavior patterns, uh, that could be related for financial industry clients, uh, to specific financial products that their company offers. And after that, once we create those segments, we can activate those, uh, in Martin Cloud engagement for, uh, more personalized marketing campaigns, where we can offer specific uh, relevant products to the client responding to their needs. And the data insights part, again, artificial intelligence steps in because we can leverage the insights it offers to identify some cross selling opportunities, predict, uh, churn, and also know what kind of, uh, financial products we can recommend to our, uh, customers based on their profiles, based on their intent and needs so that we respond to to them better.

Speaker 2: And, um, since we we introduced that that station saying how to transform the financial sector with data cloud. So we should also answer that question. What kind of financial decision do with data cloud? What kind of, uh, challenge, uh, it can solve? So before you to to answer that question, like, deep into the the the specific margin challenges that they they could face, uh, some of them is not exhaustive, but you can have, uh, issues with the the compliance, uh, regular marketing challenge, uh, the brand consistency, the lack of customers trust. So for this, you can also rely on a marketing, uh, stack, uh, of of Salesforce. Um, we we also see a lot of, um, challenge regarding digital first customer, uh, and I talk about the creative industry. The also the way the marketing tech, uh, is evolving. So be sure to to select the partner that can also be, um, innovative and that can support your growth in terms of usage. And three main, uh, challenges that we see, personalization, uh, big data AI, and, uh, commodities commoditization of your product. We will see specifically in some of the use cases, uh, what you can do to, leverage segmentation and decide what customer to be served and how to make your communication unique. And, um, another another number here, uh, is the person receiving gap. Uh, actually, only 31% of their marketers in their financial services, uh, feel that they are effectively leveraging customer data to personalize, uh, the offers. So first, do they have enough data? And if they have enough data, they don’t really use them. So let’s see how we can do to make it easy to capture more data and to leverage those data.

Speaker 1: And here comes our first use case that we want to introduce you. So, of course, it focuses on financial industry clients, so how, uh, such clients could use the power of data cloud. And as I mentioned, the capabilities of the products, so using data cloud segmentation capabilities, let’s say the, uh, bank or, um, uh, some other financial, uh, industry client could use can create dynamic customer segments based on this unified data and predictive behavior. So let’s say that the bank offers different investment products, but, uh, this client struggles to personalize this offer. So, basically, Data Cloud could help, uh, to know which, uh, clients, uh, should we target with which personalized investment products. And as you can see on the flow, uh, based on ingestion of data coming from offline and online resources, like email, web, mobile, service, uh, branch interactions, etcetera, we could build the seed, uh, segments. And out of the segments, we use and leverage, uh, calculated insights feature to know whom should we target better. And then with the power of the, uh, activation, uh, and, uh, let’s say, personalization features, we know, okay, which products should be, um, attached to each customer profile and we should personalize on.

Speaker 2: Uh, talking about personalization here again, the the question of how to make that communication more, um, unique. Uh, it’s not just about recommending a product. It could be also recommending a service and, um, how to fine tune, uh, your your communication to, uh, thena segments, and those segments could also be recommended by the ai of the platform itself. So let let’s see what we can do for, uh, to augment the the personalization.

Speaker 1: The other use case that we might suggest is the predictive account type selection. So we can develop a predictive analysis system inside of data cloud to determine the types of accounts customers are likely to opt into. As you know, the banks can let can, uh, offer different, uh, products and services. So, uh, this kind of scenario will leverage past behavior and events, uh, that customers might have to make, uh, informed predictions and drive decision making for personalized marketing strategies. And, basically, this analysis, uh, helps to enhance, uh, the marketing ability to deliver tailored communication and offers for different account types. For instance, we can divide our audience into savings account and investment accounts and, um, others. And, uh, again, as you can see on the flow, we are using, uh, the, uh, data ingestion part. We use the power of calculated insights to bring, uh, to, uh, enrich our segments and customer profiles and then activate on this data through marketing activation platform, uh, accordingly, uh, with the personalized offering. And, of course, the result is so that it drives, uh, the rate of opting into a specific account and drives customer engagement. And, uh, to have some sort of summary of today’s session, we have prepared some, uh, the, uh, some of the highlights. So what data cloud can do. So definitely, data cloud provides that unified omnichannel customer view by connecting different data sources and unified in, uh, into, um, individuals that you can then target on through marketing effort. Data cloud also eliminates organizational silos, so fostering collaboration between teams and department because in this platform, different teams could work together and, uh, find relevant information, uh, for, uh, their needs. Uh, Data Cloud also enhances marketing effectiveness because we use the platform and its connected data, uh, to activate our highly targeted and personalized campaigns that could help us to, uh, build better customer experiences. And we all know that, uh, uh, customer experience becomes even more important than just the products and services that you offer. Uh, data cloud increases, uh, the customer lifetime value. Or, basically, in financials industry, it means that, uh, people are converting or clients are converting into more, uh, services or products. So so it basically calculated products and services used by one individual. And, uh, Data Cloud also empowers marketers in their daily job to leverage a large dataset without the need for dedicated data analysts because, um, Data Cloud is very self intuitive tool. So once everything is connected and prepared from the data part, uh, then marketers could work on it to build the segments faster and in a more efficient way, uh, because the, uh, user interface activation. And, uh, we want to thank you for your time today to join our session. Uh, we have prepared, uh, the QR codes if you’d like to connect with us and if you want to, um, address some of your questions or, um, challenges. Uh, we are happy to help you in your journey. Uh, thank you so much for joining us today. I will keep Vicky our call.

Speaker 2: Thank you.

Speaker 1: Okay. Do we have some time for the q and a?

Speaker 2: I don’t

Speaker 1: think so. Is that safe? Okay. Good?

Speaker 2: Good. Perfectly. Stage. Okay. Yeah. Traffic and stage.

Speaker 1: Thank you.

Speaker 2: Thank you.