Speaker 0: Hi, everybody. Uh, Melissa, I think you’re still on mute. And welcome, everybody. It’s great to see so many people here in the chat and getting us started. So I think we’re live. Let’s get get into things. Lots to go through in a half hour. So, um, welcome to our session today. I’m really excited to be talking with the wonderful Melissa Hildes, and we’re going to be presenting about Beyond the Hype, Let’s Talk About Real Use Cases for Data Cloud. So this is one that I’ve been waiting to present, um, ever since we got the idea together. And just a huge thank you to start us off for all of our incredible sponsors. We wouldn’t be able to be here each and every year coming back to MAR DRAMING if it weren’t for all of the wonderful organizations that we see on the slide here today. So take a moment, check them out. And I am going to start by introducing Melissa. So we’re doing this a little bit differently today. We’re gonna introduce each other because who likes to talk about themselves. Melissa is, um, with Agile Consulting. She is a digital transformation strategist. And when I think of Melissa, I think of the hashtag that she uses everywhere, brilliant, beautiful, bold. And I love how she is always the first person to support everybody else around her in the Salesforce community. So we’re so lucky to have her here, um, cofounder of Nonprofit Dreaming, Foodforce, wrote a book, so many impressive things.

Speaker 1: Thank you, Vicky. You’re so kind, um, and I I am excited to be here with you. I’ve never gotten to co present with Vicky before, so this is, um, a huge honor for me. Um, Vicky is with SAMA, specialist at marketing automation. One of my pet peeves is, um, acronyms because I can never remember what they mean. Um, she’s the head of architecture and enablement, a Salesforce MVP. She has been such an influence on so many people. She’s a certified instructor, does training, and has just I have watched her over the last eight years grow into this amazing, brilliant, beautiful, bold human being. And she really is, um, all of that plus she is an exquisite mom to three wonderful children. And so you know that she’s got project management at the forethought all the time. So love to love to present with you. This is a great opportunity. Thanks, Vicky.

Speaker 0: Thanks, Melissa. Cool. So our goals for the session today are going to be going into the what and why. Like, what is data cloud in the first place? It’s not all of the session, but just to get a foundation under us. And then going beyond the hype, really understanding some of the use cases that we’ve seen out there in the wild, and then talking about future proofing. So hopefully walking away with a lot of valuable information today. And what does data cloud do in the first place? So we just have this image that kind of gives an idea of all of the different data sources that you can be connecting. We use data cloud to harmonize our data together, get to this single source of truth that really one view, three the true 360 degree view as we call it, with identity resolution, and then being able to act on our data. So I know that we were talking about this before when we were prepping the session, and it’s funny how we naturally get attracted to different parts of the process, the platform. So I know I was on the end side of this, really enjoying acting on the data and all of the things we can do. And, Melissa, you were much more on the the excited about connectors and ingesting all of the data.

Speaker 1: Right. And harmonizing it. I think that’s and probably I thought about this overnight, Vicky. Um, I wonder how many and I don’t I’m assuming that we can interact in the chat. Is that correct? Yeah. Wondering how many of you that are listening have delved into data cloud at all yet? Because I work a lot with nonprofits, and sometimes they are slower to adopt. And so I’m just wondering, you know, from connecting and harmonizing and breaking down those silos, that’s how I see data cloud really working its magic, right, for everyone. And I’m not sure how many folks are already looking at that, doing something, achieving something. Just curious if you wanna oh, yay. Great. So just wondering, you know, if if you’ve thought about it yet and if you haven’t, you know, why not? We’d love to hear.

Speaker 0: Yeah. I’m seeing the website SDK. I’m so excited about that. Mhmm. It is, like, helping to get all of those website engagements and everything like that. Cool. Lots of 2,005 projects. So I see some New Year’s resolutions in here.

Speaker 1: Twenty twenty five projects, not project twenty twenty five. Right? Yes.

Speaker 0: Absolutely. So now we’ve seen a little bit what it’s about. Very light touch there. There’s so many other sessions that the, uh, March dreaming that will give more information. We’ll have some resources at the end if you wanna learn more too. And now what is the hype all about? So we’ve kind of structured this in different areas. And the first area that we saw really adding value to the platform was making your CRM more efficient because many of us come from the CRM side. That’s when what we know, what we love, our end users are already there and using it. So how can we build on top of that? How can we make it more efficient and really build out this view that they’re already using? So one of the things that I know I’ve seen is saving and visualizing our marketing interactions. So just something as simple as a nice little timeline that we can add to the contact record with who has opened what, who has clicked what, all of those really useful information. And for any of us who are in the marketing cloud platform, the data view tables, they save all of our marketing engagement data and those interactions for six months, and then we have to store that somewhere. So I’ve seen customers in the past pushing that data out into data warehouses where it’s getting stored but never acted on, we’re never using it again, who actually goes into their data warehouse and actually ends up using that data. It’s there for a rainy day, but that is where it stays. So being able to act on that, bringing that into conversations so that our sales reps, our support reps, whoever’s interacting with the data already in the CRM can visualize that data is so powerful. And then I remember from my very first conversation around data cloud with Melissa, the next one about reluctant adopters. So I’m gonna pass it over to you.

Speaker 1: And so, you know, I’ll plead the fifth here. And if there’s anybody who’s a huge fan of razor’s edge, you know, don’t hate me. One of the biggest experiences that I’ve had with data silos is working with nonprofits with donor relations, right, and donor funding. And Raiser’s Edge everyone has Raiser’s Edge, and everybody hates Raiser’s Edge. You know, nobody I’ve talked to is ever like, we love razor’s edge, but nobody wants to migrate from razor’s edge to Salesforce. And so that has been a constant area of concern and, you know, how do you make decisions in Salesforce if you don’t have your donor data there? Right? So you either have to, um, you know, write a custom integration or pay Raiser’s Edge for their integration. With data cloud, the great thing is now I’m like, okay, fine. You all who hate Raiser’s Edge so much, right, but you refuse to leave it, stay in Raiser’s Edge. But with data cloud, we can access that data so that we can put together executive reports, executive summaries to drive decision making based on all the data. Right? So your your volunteer data is not in one platform and your donor data is in another platform and your payment data is in another platform. With data cloud, we finally really pretty simply in most cases can break down all those silos.

Speaker 0: Absolutely. And there’s even, like, Google Sheets connectors and beta and things like that. So we’re really going to be able to see the the people who love spreadsheets, go ahead and use your spreadsheet. That’s that’s okay. But we’ll just leave you there, but we’re gonna be able to leverage that data. I love that. And then, um, I know one of the projects that we’re actually working on right now, it’s a client who’s doing a CRM migration and who is going from an org that was spaghetti code, like many of us know, um, orgs that have been around for a while, really mature, built up quite a bit of technical debt, and working with large amounts of transactional data. And we’re actually seeing data cloud being used to store the data somewhere else. So we don’t have to worry about getting that data, the large data volumes, all of those considerations into the platform itself. We don’t have to save it in Salesforce. Data cloud’s saving everything what we call near core. So it’s actually not on core. We’re just virtualizing or visualizing that data in the CRM when we need it. So it’s making life so much easier and just making it a bit safer for us as well. We don’t have to have all of those considerations and worries in the new org. So, um, we have some funny GIFs in here because who doesn’t like a little laugh as we’re coming through and talking about data cloud marketing, all of that good stuff? Do you know who I am? All of our customers expect us to know who they are, and it’s surprising how many people don’t know who we are. So Melissa, I know you’ve gotten some really great data for us. Do you wanna talk us through what we have here?

Speaker 1: Sure. And this is this is really you know, it wasn’t a scientific survey or anything. I have presented on data cloud at several different, um, dreaming events across the ecosystem this whole year. And so as a point of reference, I just I ask, you know, people some questions. And so it’s a mix of folks. But if you think about it, how many external applications does your organization use that don’t talk to Salesforce? Internally at Agile Cloud Consulting, we have, like, 30 that we just came up with off the top of our head that we use that don’t necessarily feed any data into our Salesforce instance. Right? And you need all of that data. You can see, you know, 47% of the folks that we surveyed use at least one to six external sources. You know, are you capturing that data? Are you getting it in your Salesforce? Are you taking it into consideration as you go? Um, from from Salesforce itself, the survey that they did, you see the average enterprise uses 991 applications. I can’t believe that. Maybe we’re not as bad technical debt wise as we think. But it was just important to understand that if it’s not connected or can’t access that data from Salesforce, then if that’s where you’re going to run your reports, if that’s where you’re gonna use AI to help you analyze data, if it’s not there, it can’t analyze, you know, what it can’t access.

Speaker 0: Absolutely. And I know even from a marketing side, it’s those hidden systems too. So even when you ask people what systems they’re using, they’ll tell you a couple systems, and that’s what they think their data is stored in. And then as you start to discover more, even ask, do you post on social media? Do you do paid advertising? Anything like that. You just uncover all of these different systems. Nonprofits with fundraising pages. How many different platforms do they have with fundraising pages on them?

Speaker 1: And and knowing who someone is. Right? So I’m sure you all if you’re if you work with a Salesforce partner and you, um, go into Trailhead and the the identity resolution there that’s not been perfected yet, I don’t think. You know, it’s like that was the whole thing. Don’t you know who I am? You know, it’s if I am interacting with you on any of your 991 platforms, I shouldn’t have to tell you who I am. You should know who I am. That’s the customer experience that I’ve come to expect. Right? So why would I give you my email address again? Why would I give you my mailing address again? If you don’t have all that, um, based in one single source of truth, then you don’t know who I am, and that’s frustrating to me as a customer.

Speaker 0: So true. And that actually brings us very nicely into identity resolution for the win. So if you remember back to our image of data cloud, it was that central image with the picture of a face and all of the different identities that we can have for somebody. That’s identity resolution. So really being able to say, every system’s got a unique identifier. Let’s knit that into one version. So, um

Speaker 1: Sorry. Go ahead, Vicky.

Speaker 0: Oh, go ahead. I’m just gonna hand it over to you. So why don’t you tell us about our consolidated interactions?

Speaker 1: Yes. Um, and I love that you said unique identifier because, you know, traditionally, the email address has been the unique identifier. And think about how many email addresses you have. Right? I mean, back in the day, way back in the day, everybody had one email address, maybe two. Maybe they had a personal one and a work email address. Now, I personally have at least 15 that I need to interact with on a regular basis. You’re right? Between my Salesforce MVP, my community, um, user group, my work email, my personal email, my on and on and on and on. And so reconciling your identity, probably the single thing that I have that is unique to me besides my social security number, right, which you don’t wanna deal with, is my phone number. So you may have two phones, but unless you’re a high level drug dealer, I don’t know anybody that has more than two phones, you know, a work phone and a cell phone. And maybe that’s a consideration for identity resolution. But that’s one of the best things in my mind that data cloud can do is when you bring in all that data from all the disparate resources and access it, you can resolve my identity based on who I am. So that when I check out a new product on Salesforce, you know, an AE doesn’t call me because they realize that I’m not a potential customer. They realize that I am a, um, a Salesforce MVP, and I’m probably just doing some catch up work. Right? So just important that we’d be able to really know who someone is, even something as simple as a donor who is also a volunteer or a volunteer who is also a donor. Right? And for your fund development department, if they’re volunteering ten hours a week with you every single week, they’re probably a good person to approach, right, um, about making a bigger donation. But if you don’t have that consolidation to know that that’s one in the same person, then you can’t make that leap and you can’t analyze that.

Speaker 0: Oh, for sure. And that actually brings us really nicely into the next one of getting the actual numbers. So this was one of the first use cases that I heard about when I was teaching the data cloud classes for one of the biggest customers out there. And, um, I won’t name any names, but getting to the actual numbers. So they have different orgs, and many, uh, customers do end up having different orgs, whether it’s through mergers, just through different processes that they have in different regions, it may be compliancy. But having those Salesforce orgs and getting the actual numbers of unique customers, especially if you have people like myself who live in France, who travel back to The States, who travel to The US. And when I want to ship things to different places, I have a version of myself in all different regions, all different companies. And it led to that really disconnected customer experience, like you were saying before, having to use different identifiers to log in, maybe even different levels of service in one region that didn’t apply to another region. And, um, after after I taught the class, after I saw this implementation happening, went on to my ebook reader. And, usually, I had to go back to The US site because I like to read in English. So I would always have to purchase my ereader books from that site. I went and purchased accidentally from the French site and went, oh, no. Now I have to read the book somewhere else. I can’t read it on my e reader because usually it breaks. It tells me it’s not in the right country, and it worked this time. And it was just that magic moment. Like, yes. This is actually the experience that I want to have. So we’re starting to see that as a result of tools like DataCloud.

Speaker 1: And the unintended consequences of getting it wrong, if you go to the next slide, Vicky, I think that’s probably the best information for getting it wrong. Right? So this is, um, I I literally in a lot of my presentations, I have asked specifically nonprofits, you know, how many of you have ever been on a donation site getting ready to make a donation? You know, you’re probably sitting on your couch at night watching the television and you’ve got your cell phone out and you’re getting ready to make your donation. And, you know, ten minutes later, you’re still trying to enter information or enter um, your credit card number because they don’t sync with Apple Pay or Google Pay or something, and you just give up and quit and don’t make the donation. And when I ask people to raise their hands, literally, everyone in the room would raise their hand. It’s just too hard. And that’s terrifying that we’re not doing a better job. How much money are we leaving laying on the table because we’re making it too difficult for people to donate? And so having all that data and knowing who I am, making it easy, that’s what data cloud helps us do.

Speaker 0: Yeah. And I remember an example you gave as well, even around community events, like we’re all sitting in today and speaker submissions. If we wanna limit speaker submissions to five submissions per per person, but we’re not consolidating somebody’s emails addresses, it’s pretty easy to either purposely or unintentionally get around that and be submitting more than that. And that goes for anything where you’re voting, where you want to limit somebody’s responses. We should know all of their email addresses that they might be using and say, oh, nope. You have already voted, so you can’t vote again. And, um, I have a book that I talk about all the time. I’m sure some people in the audience have heard me talk about this. It’s called Don’t Make Me Think by Steve Klug. I’ll put this in the chat as well. It’s about website development, but that concept of don’t make me think, we have short attention spans. As soon as somebody has to think, we’ve lost them. So, um, this ties in very nicely with the data that you got from the next question on your forms, doesn’t it, Melissa?

Speaker 1: Yes. So, you know, 87% of people surveyed said that constituents, customers, donors interact with you via two or more external applications. And I love, love, love Salesforce’s data that says that 80% of customers say experience is just as important as products, and sometimes I think more so than products. Right? Um, I just I don’t have time these days to do anything that takes immense amounts of times. So simple, easy, it may be complicated on the back end, but as long as it presents simple and click a button and you’re done on the front end, that’s that’s what customers want, and that’s what data cloud can help you achieve.

Speaker 0: Yeah. And it ties in with that idea of human centered design. Right? Keep your humans at the front. Let’s take all of the complexity on the back end. And I know we’re coming towards the end, but we have some really good information about future proofing your investment. So we are talking about data cloud. Data cloud is an additional purchase unless you’re on the free scale, but you might be purchasing credits. We really wanna get some benefits, some return on investment based off of that. So we said preparing for your future. We need to be able to square your data away, and that’s what data cloud is helping us to do. It’s helping us to get our data cloud in order to do so many other things. And Melissa is one of the top go to people, at least for me, when I wanna learn about AI or when I wanna talk about AI. So what have you seen as far as needing our data to be in the right place?

Speaker 1: Sure. And so there are really three well, two parts of getting ready for AI. And I think this is where most of us are. Um, we’re getting ready for AI. There’s a lot of talk out there about it. There’s a lot coming. Agent Force is exciting, um, and I’m presenting at world tour next week on that AI readiness assessment. So you need clean data, right, which you get by doing some data cleansing, some data hygiene. You need complete data, which I data cloud to me is the single best way right now to get complete data, to get all your data in if not in the same place, at least accessible right to the same place in order to be able to leverage AI. So that AI is only gonna give you the results based on the data that you give it. And my biggest fear is that we are so excited about AI that we don’t do the proper prep, which is cleaning up that data, getting all the data together, even enriching your data. Right? So there are APIs out there right now that are available for things like, um, Candid, which, you know, in The US is the go to for nonprofits to be able to pull all that information together. And that way your AI can give you good results, not hallucinations, not, um, inaccurate data, but it’s really grounded in the good data that you’re providing for it.

Speaker 0: Absolutely. And we were even talking yesterday, um, when we’re doing some last minute prep together, about all of this data that we’re feeding into AI and feeding into agent force. And going back to that idea of the unintended consequences of getting it wrong, what happens if we feed dirty data into agent force? We don’t really know what the answer is yet. So if we’re training our models on data that is not clean data and not a cohesive view of what our data should be, then that can have some consequences that we’re maybe not expecting or ready to to to take responsibility for. And this last one is about all of the products and future products that we’re going to see on the road map coming from Salesforce. And to some degree, we’re already seeing this with the Salesforce foundations, including marketing cloud growth, marketing cloud advanced edition, this idea of marketing cloud on core now, we’re seeing that that’s really built leveraging data cloud. Agent Force is built leveraging data cloud. And all of these future connectors may be being replaced, the tools that are moving on to the core platform to be able to better leverage our data, you’re really prepping yourself for everything that did Salesforce is going to be bringing us. So we have a nice quote from Melissa. I will hand this over to you.

Speaker 1: I I really got tickled. I thought about this one day and just wrote it down and presented it, and several people have quoted me on saying this. And it’s really true. Right? So if you’re expecting AI to make your data sexy and it’s ugly, it’s not gonna happen. It’s gonna make it uglier. And And not only is it gonna make it uglier, it’s gonna make it uglier faster. Right? So that’s what AI the good thing that AI does is it speeds things up. The bad thing that it does is it actually exponentially does things. So if your data is ugly, if it’s incomplete, then AI is just gonna make it that much worse. So you’re not to be not to be disappointed in what AI can provide because it can be a phenomenal tool, but you have to do the prep work first. Right? And the consequences of getting it wrong, you’ve probably seen some of the horror stories, um, you know, in early AI attempts where things went horribly wrong and people did not wanna use it. So I would encourage you put together the the the not so much fun stuff first. Right? So that when we get to AI and implementing that for you, then it’s going to do what you want it to do and what you expect it to do instead of something untoward and scary and not good consequences.

Speaker 0: That’s so important. We have to do, like, our homework first, the things that we don’t wanna do. Right. We have to get it out of the way. And for anybody here who wants to learn more, we’ve put some resources together. And I have Gilda’s guide to data cloud resources as the first one in here. It’s a LinkedIn post, and Gilda has done an amazing job of consolidating everything together. And she’s given us links that even though the post is from 2022, um, it’s still very much so valid today. So I will share the, uh, link to this entire presentation with the chat here in just a moment. And that way you can click in on all of these links to really find what it is that you’re looking for. There’s a nonofficial consumption calculator. I saw this, um, in the mentioned in the chat before, and even an AI readiness survey. And I know Melissa has done a session on consequence scanning that I absolutely love in the past. So we’ll be linking this in here too so that you can go back. You can watch what how to perform a consequence scan. And I think that with all of the power that data cloud and AI are giving us, it’s gonna be a really important tool to be able to do those types of scans and assessments. So thank you all so much. I know we have two minutes left. So thank you all so much for joining us. Thank you, Melissa, for presenting with me.

Speaker 1: Thank you.

Speaker 0: And, yes, we’ll go ahead, and I’m going to make this smaller so that I can get to the link. I can share it in the chat with everybody, and we are so happy to share all of our resources. So feel free to check out the Google link. The you have the entire presentation there, and thank you for joining us today. Make this nice and big for a thank you at the end.