Speaker 0: Alright. Well, welcome everybody, uh, for today’s session. Just wanna check to see if anyone can hear me. Uh, let me know if you can’t. Um, okay. Excellent. Uh, so welcome everybody to our session today on how to make Data Cloud, uh, data actionable in Marketing Cloud. So I’m Tim Zider. I’m a manager at Cervelo. We are a Salesforce partner, uh, helping firms, uh, stand up marketing automation solutions for their business. Uh, let’s see. Let’s go to the next slide. Uh, so I just wanted to, uh, say thank you to all of our incredible sponsors. Uh, please take the time, uh, to take a look at all of their great offerings while you’re at the conference. Without them, we couldn’t make this happen.

And let’s talk about what we’re gonna be going over today. So we’ve got, um, to do this setup of creating that data actionable in marketing cloud from data cloud, We’ve, uh, created a use case, uh, to walk you through and some data real life data to to work with. Um, we’re gonna go over how this use case, um, this data action part of data cloud fits into the overall flow of data cloud. And then we’re gonna go right into the platform and, uh, depending on time, build out a segment from start to finish, see that data, go right into marketing cloud, and create, um, and view some custom automations and journeys to bring that to life. And then we’re gonna do the same for data actions. So it’ll give you two different options of how you can take that data from data cloud and send it into marketing cloud to make it actionable.

So let’s talk about this use case. Um, so Cervelo Bank has launched a new mobile trading app, and they’ve done an all out marketing blitz to try to get in new users, uh, to make trades right on their phone. Super handy. Super cool. Um, the problem is a couple months in, they’re noticing that sign ups and trades are about 50% lower than expected. Um, so they wanted to sort of address that problem. Uh, one of the problems is is this mobile data that’s on their app is very siloed. They have no way to directly pull that into their Salesforce CRM environment, uh, much less marketing cloud, and so they’re finding that’s a challenge. And then the communications on the mobile side are also very limited. There’s no real way to email them. There are app notifications, but they wanted to really leverage the power of marketing cloud to communicate with these new sign ups to get them to trade or to get some new, uh, you know, leads into the funnel.

So they, uh, what they wanted to do was target we’re gonna use this use case today to target two segments, um, in their sales their customer funnel. So the the way this Cervelo Bank use case breaks out their customers, they break them onto two groups. You’re either a prospect, someone who have signed up for the app, um, have started to to, um, you know, interact with the app. They might have put an account, you know, created an account number, um, but they haven’t deposited money into into that account. That’s usually, like, sort of separates a prospect to a trader. Now within the prospect, um, window, uh, there’s a big bottleneck, which is getting those people who signed up to link their bank account. You know, once they link that bank account, they’re more likely to make that first trade. So that’s the first segment we’re gonna go after today is how can we isolate those mobile traders who’ve signed up, maybe created an account, but haven’t banked their link. Let’s see if we can get them to motivate them to link that bank.

Now the second half are these traders. Right? These people who’ve they’ve linked their bank. They’ve I mean, they they may have deposited that money, um, but, you know, then they’re starting to trade. So same thing within this group. One of the big bottlenecks, uh, within those traders or traders who are starting out is, you know, they’ve we’ve got a large group of people who’ve made that deposit. Right? They have money in the application ready to make that trade. They just haven’t made that first trade. So that’s the second group we’re gonna wanna isolate are those people who’ve deposited money, um, but have not made for those first trades.

Okay. Let’s just take a look at all of this data that’s coming in from data cloud that is gonna sort of set up this use case. Um, so we’ve got in the center here, we’ve got data cloud, which is pulling in all this data. And then over here on the right, we’ve got that mobile data. Like we said, it was very siloed. Well, now we’re gonna bring that in to feed that into data cloud. That’s one source of the data. Right? Those people who signed up for the application. Another source we’re gonna use is as an example, like, using you might use this enterprise data from the bank. It could be used, uh, ingested based from, like, an an Amazon s three bucket. So this would be if you’re a banking customer, you might have a checking account, you might have an investment account. Um, and this brings in all of that information so that can be matched up with their mobile app, um, information on the right. And then a couple other Salesforce data points. Because Salesforce is the system of record, contacts and leads are managed in the Salesforce CRM environment, so pulling that in as well in the data cloud. And then last but not least from marketing standpoint, because the goal of this is to send them out communications, we’re gonna be bringing into data cloud things like the subscriber key. And then also later on and then maybe a different use case, bringing in that email engagement to, uh, help drive that model. So those are all the different data sources that are coming into, uh, data cloud that we’re gonna be referencing today.

And then let’s take a look at how this all works together in that data flow like we talked about at the beginning, what part the data action side is gonna play in the role. So you can see here on the left, we’ve got those four different data sources that are coming into data cloud, that enterprise data, the mobile app, etcetera. We’re gonna be creating those, uh, we’ve already created those data streams in data cloud for you. Didn’t wanna go through that exercise today. And then we’ve also gone through the exercise of mapping each of those streams so that that they can work with the model for unification. Right? There’s a specific model. You need to map all those attributes from all of those different data sources so it aligns with this unified profile, and that’s that third column under unification. With that unified, now we’re gonna be able to make those different segments that we’re either gonna use the segment part of data cloud or the data actions to drive those marketing actions based on that unified, uh, individual in data cloud.

And quickly, I wanna go over the model, um, in data cloud that helps you align all those data sources because you’ll see this in action today. Um, so when you were bringing in those different data sources, you have some required fields in order to make it work, uh, in that data model. So in our example today, if we’re gonna looking to communicate in marketing cloud via email or maybe by phone, we’re gonna have a couple of requirements here. First of all, we need that individual object at the top. We’re gonna also wanna make that connection to the contact point email and the contact point phone, and then also the party identification will, um, will be helpful in that way as well. So it’s just good to know that within data cloud, when you’re doing unification, it’s not just a single object, it’s a couple of objects you need to align your data in. You’ll see that in play today, so I wanted to call that out.

Okay. So let’s take a look at how we’re gonna be driving these two use cases through the two different ways in data cloud that you can send that amazing data through to marketing cloud. So at the top here, we’ve got our no bank link. Right? These are those customers who’ve created an account. We want to start communicating them to say, hey. Can you link your your, um, your bank and then take the next step? So on the left, for both of these scenarios, we’re gonna be pulling in that unified individual. So at the top, we’re gonna be creating a segment in data cloud, and then we’re gonna use two different steps in that process, an activation target, which is going to point to marketing cloud to say, okay. This is where the segment is gonna be written to. And then the activation object is going to indicate what are those fields that we want to populate and, uh, and send those values into a data extension in Marketing Cloud. So with all of that set and published, it’s gonna end up creating a data extension in Marketing Cloud in the shared folder, which we’ll go through as well. And then we’re gonna be using a custom query to query that data extension, which is gonna feed the journey. Now we wanna cover three cool ways for how we’re gonna be leveraging this data in marketing cloud. The first data that we’re gonna use is to segment to determine who should be entering in this journey. That’s very important. The second data point is how we can personalize this email messaging or phone messaging, uh, once they’re in the journey itself. So using those data cloud data points to do that personalization. And last but not least, the third item is to use that data cloud data to determine the different paths they should be going on through the journey. So hopefully, we’ll be able to deliver all three of those, uh, today in both of these examples.

The second example, the lower part of the slide, is the drive first trade. So this is that lower part. These are those traders who, um, you know, they’ve they’ve deposited money on their account. They just haven’t made that first trade. So we’re gonna go a different route here. We’re gonna create a calculated insight. Now that calculated insight is going to determine how to get to those key values, that subscriber key, the email address, and it’s gonna include all those attributes that are going to, um, be moved over to, um, instead of creating a data extension alone in marketing cloud, we’re gonna be firing these subscribers directly into a journey itself using an API entry event. So we’re gonna create a calculated insight. Based on those settings, we need to and then build a data extension to align with those data values, set up this API entry event. Then we’ll come back into Data Cloud, and we’re gonna create a data action target and then the data action itself, publish that so that whenever a calculated insight is run where we have someone who’s, uh, deposited, uh, data excuse me, deposited funds into their mobile app, we can instantly send them an email message so that we can try to get them to convert and place that first trade. So we will send that to the API entry event, and then, uh, we’ll that’ll naturally pull them right into a journey and then have that same level of personalization and, uh, using that data to create those different journey paths as well.

So let’s talk about so the segment, what we’re gonna we’re gonna walk through here today. So first, we’re gonna create that segment. We’re gonna create the activation target. The activation itself is going to determine all the different attributes that are going to be pop populating that data extension. Then we are going to publish and then validate that data gets populated in Marketing Cloud. Once we’re in Marketing Cloud, we’re going to take a look at that query automation and show that dynamic content and even walk through a custom attribute in Data Designer that you can use in your journey paths.

Okay. So we’re going to hop out of this slide deck, and we’re gonna go into data cloud itself. So let’s start with, um, segments. We’re gonna create a new segment, walk through this, creating this from scratch. So we’re gonna go to the date the segments, uh, tab in data cloud. We’re gonna click new. And, uh, just give this a second to refresh, and we will start creating a segment. So we’re gonna use a standard segment. We’re gonna use this visual builder, um, application, the default settings. Uh, now I’ve got a name here that I’ll just make sure that I use consistently across all other, um, all the the the, um, assets that we’re building here so they’re all the same. We’ve got a name and a description that we’ll just copy over here. And then here’s an important thing just to note. We wanna use, uh, whenever you’re you’re creating your segments, recommended to use the unified individual first. That’s gonna be that individual that has all those key data points that have been unified. Start there. You can always go back to, um, individual or lead or contact, but this is usually best practices. We’re gonna like I’m probably gonna choose the rapid publish in this example, um, because in in this example, we wanna send that email to somebody who’s created an account but haven’t bank linked. So as soon as they’ve created that account on the application, we’re gonna wanna send this message really, you know, relatively soon thereafter. So we’re gonna probably want to use, uh, a more rapid publish in this example. So we’re gonna do this on an hourly basis. We’re gonna set this up to start today. We’ll have this start maybe around let’s do that around 02:00, and we’re gonna do the end date at the end of this year and hit save.

So it is creating the segments. Give it a second, and it will populate in Data Cloud. And so what this is populating here is 1,800 unified individuals in our dataset. Right? It’s a little bit narrow. Our our our volume is pretty low because it’s just a sandbox, but this is that curated list of just those unified individuals. Now we can further refine this, um, to create that segment that we’re looking to populate. So let’s pull in this account terms agreement. This is basically a flag that says that, um, we’ve, uh, we’ve got an account. This person has created an account or they have an account currently on that enterprise data. So that helps narrow that things down a little bit. And then let’s add this bank link in here. So we want those that have not bank links, so that’s false. And then one other way to just show the flexibility of this content, we also wanna include a bank link that has no value. It all depends on the dataset, so we wanna include both of these options. And what’s nice about the functionality is if you hover if these two are next to each other and you hover over this, you’re allowed to click on this and select that or statement. So you can, you know, have a couple more a little bit more flexibility in the terms that you’re using for your segmentation. Okay. So once you’ve set up your segmentation, you hit save. That should rerun these numbers and give you a little bit more of an accurate picture of the audience that you’re looking to target. Just gonna grab some water. There we go.

Okay. So we see that narrowed down from 1,800 unified individuals to a a a much more targeted subset. So there it is. We’re creating that that segment. That’s part one of how we’re using data cloud to use that to create those custom segments in in to drive this, and then we’ll see that populated in marketing cloud. So we are all set here, um, and save. We can just hit done, and then this should have popped up with our screen to show us, uh, this segment that we’ve just created. It shows all the details that we just created, so we are good to go there. We’ll come back once we’ve created the activation target and the activations to, uh, to publish this.

So next step, let’s go to activation targets. And let’s go in and we’re gonna select new, create a new activation targets to walk you through this process. Pretty straightforward. And, um, we’re gonna again, this is what is determining where this segment is gonna be written to. So we’re gonna wanna select marketing cloud. It doesn’t really tell you specifically, but we all know that icon well. And then we’re just gonna name this the same name that we’ve been using for all our other assets. And we wanna select this data space, which is we currently just have one right now, but you might have others, multiple ones in your environment. This is the business unit that’s attached to our our Marketing Cloud business unit, so good to con confirm that. And that should be it about setting up an activation target. So pretty straightforward from that standpoint. Easy stuff. Again, just connecting this segment to we wanna write this into Marketing Cloud.

Next up, we wanna create an activation. So this is going to tell pull everything together essentially and tell Marketing Cloud, uh, what are the fields that we wanna populate, uh, on that date extension. So we’re gonna click new for this activations, and let me just copy this name so we’ve got that ready to go. We’re gonna use the segment, activate data from a segment, click continue. We’re gonna use the default, uh, space. The segment that we’re gonna use is the same segment that we’ve created. So we wanna navigate down to that underscore nav segment. You can see we’ve created quite a few there. I’m getting an am I still being able to screen share? Let me see if I can go off video. Maybe that might help. Am I still able to screen people still able to see my screen? I got an an error. Okay. Fantastic. Sorry about that. Alright. So then we wanna select that activation target. So we’ll go back in here and select this activation target, and I’ll just give that a second. We want to select on the unified individual. Again, you can always select individual as you’re looking to open up your filtering. We want to select the email section just to fill out this a little bit more in detail. They’ll give you, based on how you set up your unification, uh, model, you’ll have a default for where that email address should come in. So that should be preset when you come to this stage. But if you wanted to add an additional source or change this source, you can go in here and say, like, for instance, if we wanted to have uh, Salesforce be a better indicator of the email instead of, say, that mobile app, um, then we would want to, you know, use this order. Use Marketing Cloud first, then Salesforce CRM, and then finally the application itself. So that’s how you can, uh, use that to modify that net that there that that criteria. Um, so with that done, we can hit next.

And then this is where we’re gonna wanna add those attributes, the data that you’re going to want to have populated in the data extension itself in Marketing Cloud. So because they added an account number, we’re gonna wanna add account number. Maybe when that account was created, we could add that as a data point. We’ll also wanna include, like, that bank link, um, just in case they have populated that to true. It’ll be good to have that as a data point. We’ll talk about that later. And then first name, you know, relatively simple sets of data here to personalize that email as well. We’re gonna hit save. We’re gonna hit next, and then let’s just name this. And, um, it’ll default to an incremental refresh. Uh, there were in the past some settings that you can use a full refresh here, but you might have options in your environment depending on on how you’re looking there. And let’s hit save.

So with that, uh, activation, uh, created, now you’re all set to go back into the segment and publish that segment, and that will end up bringing your data down into marketing cloud. So if we just click into the segment itself, give it a second to refresh, and we’re gonna go up to here to publish now, and that will publish that segment. It also will probably publish at that 02:00 time frame as well. Uh, it’s been successfully initiated. Okay.

So that completes the part in Data Cloud for creating a segment. Now let’s go into Marketing Cloud and take a look at that data that’s coming through. So like I said before, it is, um, if you’re set up correctly, this will populate into a date extension in Marketing Cloud under the shared item. So again, we’re in, uh, Email Studio, um, we’re under the date extension tab, and here is that shared data shared, um, shared items under that shared data extension. You can see this customer three sixty segments, and here are all those different, um, data extensions that have been created. Let me just pull up one from an earlier segment that I’d run just so that you can see the data. It takes about an hour for that data get to get populated. So a folder gets created based on the name of of your, um, your segment, And then in that segment, uh, folder is a data extension. So let’s just click in here and let’s take a look at this data so we can get a a picture of what is coming in from, uh, from data cloud into marketing cloud. So there are those 23 records that we saw in that segment there. And then let’s take a look at this data, uh, because as, you know, those who are using marketing cloud, um, you know, there’s some couple of points here that we’re gonna wanna be sensitive to. So first of all, from a marketing cloud standpoint, we’re gonna wanna take a look at that subscriber key. Right? And in this use case, uh, if we’re communicating with contacts as this model, um, you can see one of the benefits of using data cloud is that this, um, this mobile app user created an application where it is triggering us to communicate to them. But as we probably would imagine on that mobile app, there is no contact ID. There’s no way to link that to a subscriber key. And so that’s one of the the amazing benefits of data cloud. We pulled in that mobile user. We synchronized the account data that we’ve got here, an account number, it’s another dataset. Um, we know that this this person hasn’t bank linked because of the mobile mobile information. That’s data that’s coming in for the mobile, um, from that separate data source as well. And then we’ve also got this subscriber key which might come from Marketing Cloud. It might come from from Salesforce CRM. Now we’re seeing all of this data now in Marketing Cloud all aggregated so that when you send the email, you’re gonna be using that same subscriber key for that mobile app user that you would use if you were pulling them directly from Salesforce CRM. So you won’t have to create additional subscriber keys, um, when you’re communicating to those mobile users if that was not pulled through data cloud. So one of the huge benefits from data cloud is the efficiency of the data and how you can, uh, you know, prevent those duplicates when you wanna communicate in marketing cloud. Now you can see it’s not perfect. Right? You can see this mobile app data, uh, field is coming through. Now this is how you can you can use this information as you’re looking at your data to refine your segmentation either upstream in Data Cloud or you can always use queries in marketing cloud to either prevent sending those or finding a different path to populate that, uh, subscriber key. So, anyways, this data is coming through. This is fantastic. You can see all these different data points.

Now let’s talk about how we can take this data, um, and send them through a journey. So we’ve got an automation set up that is connected to to our journey. Right? No bank link date extension. We’ll take a look at this query and how we’re pulling in this data. It should be connected to that date extension. Um, and so so this is a pretty basic query. Uh, but just wanted to go through the exercise of showing how to bring this to life because you can’t really you probably don’t wanna use the, um, the data extension that Data Cloud is writing to. I mean, a, it’s in a shared folder, so probably won’t even be able to be used in in a in a journey. Plus, you kinda wanna keep that data separate. Um, so that’s why we created this query. It’s relatively straightforward for this use case. So we’re pulling in that data extension that, uh, Data Cloud is populating. We’re populating all those fields that we’re gonna be using in the journey itself to obviously the basic subscriber key and email address to send the actual email, um, and then some of those personalization data points. And then in our where clause, we’ve got some filtering in here. So example, we’re just looking to pull in one subscriber. We’re doing some testing. Uh, but this is where you could say that the subscriber key has to start with a zero zero three or a zero zero q. There’s some filtering in there that you could use to prevent those subscriber keys going in if they’re not aligned within your model. Uh, and then last but not least, we are using, um, a way to dedupe those people who are already have been received entered into the journey itself. So, um, we are joining the data extension, uh, here that is fit feeding the journey, right, doing a left join, and we only wanna pull in those subscribers that are not in this date extension. Um, so that prevents the duplication simple way of doing that using a left join and a a null statement. So there’s the query for how to, um, pull in those subscribers into the journey itself.

And then let’s just take a look at this journey, um, going a couple of points. Let’s take a look at this email. Well, let’s just take a look at the flow of the journey. So in this example, we’ve got, um, you know, someone coming into the journey. We might have a QA step where they need to validate a certain, um, you know, certain information like their email address needs to be. Um, in this example, email address has to be populated. We’ll cover the cover the emails in a second. Uh, and then we might also wanna include a step in our journey that is going to be checking that bank link data that’s coming in from data cloud so that, you know, we might wanna communicate with this, um, segment, send them two emails. Right? If they send their first email, hey. You’ve you’ve created an account on this mobile app. Um, let’s see if you can, um, link your bank account. You could send that for the first email. And if they don’t do those steps, say, you know, not three minutes, this would probably be, you know, maybe a week, then send them another email. But this decision split will be checking that data that’s coming in from data cloud, um, because if it’s updated to, yes, they’ve bank linked, then they would kick them out of the journey. So we’ll show you that functionality in a second. Uh, but let’s just take a quick look at this email just so that we can show, um, how you can use the deliver on that second point, uh, we talked about, which is using that data cloud data to personalize the email itself, um, using that data directly from the data cloud environment. So marketing cloud is doing a little bit of thinking here. There we go. Thank you, Marketing Cloud. Um, so we’re gonna go into preview and test mode, and we’re just gonna go and select the date extension um, that is feeding, uh, subscribers into this journey, uh, to show how this might get personalized with that data cloud data. So we’re just gonna go select that data extension and there’s that one subscriber. Let’s hit Select. And okay. So, I mean, it’s pretty basic personalization. You can do a lot more complex, um, but you just wanted to see how this carries through to the end customer. Right? This account number is probably coming from, you know, could be coming starting from that mobile application, could be driving over to that enterprise data, could be pulled in from that s three bucket at the data cloud, and then it’s segmented, unified in data cloud, comes into marketing cloud, and it gets displayed in an email from marketing cloud. So pretty cool way to see how that data transforms through, really orchestrated from Data Cloud. It makes it seamless. It’s just like any other data point. That’s the power of Data Cloud to do that. The name could be the mobile app, the, um, might be a a different type of name where you might unify and say, no. That that first name so for my example, um, I my name could be Tim, could be Timothy, could be Timmy. You know, I might prefer Timothy, and that might be coming from that enterprise data. Again, that’s gonna be a way to find the best way to display that personalization. Um, so there’s how we can use that in the email. And then just on how that gets executed in marketing cloud, relatively straightforward. We’ve just got a a code snippet, a text block, uh, code block at the top of this email. And all we’re doing basically is just pulling in, setting those variables, uh, based on those values that are in the date extension and then just using the v function in the content itself to display it. So relatively easy from a an AM script standpoint, but, you know, if you’ve, um, there’s a lot more that you can do from a customization standpoint, but just wanted to show those values coming through.

Okay. The next example we wanted to cover is how you can use data cloud to carve out those different journey paths using this decision split as as as another basic example. But let’s just go take a look at this decision split and, um, and take a look at how this is set up. Because I think as as you know, if you’re a journey builder in marketing cloud, the data of that subscriber when they enter in that journey is stale. Right? It is only, uh, as good as the minute that they entered in that journey. But what we wanna do is, you know, seven days later after they’ve received that email, we wanna check to say, hey. Has this person, uh, bank linked or not? And, unfortunately, that’s not gonna be in the data extension here in this journey. We need to find another way to get to that data. So what you can use is, again, you leverage that data cloud data to create a custom attribute to, um, populate that data in a way that, um, you can access in in Journey Builder. So, um, let me just show I think while it’s it’s refreshing, I’ll go over to contact builder to show that custom attribute that’s created. Um, I think it’s in our second chain here, this no bank linked custom attribute. So relatively straightforward. Right? All we need to do is to create a custom attribute in data designer and pull in that data extension that we’re pulling in from, um, you know, from that shared data extension. We might have to depending on your setup, you might need to, um, update a data extension within your environment to do that. But, again, it aligns it aligns that subscriber key to that bank link field that the data cloud is updating on an hourly basis to using that segment. So if we go back to, um, Journey Builder, what this is doing is is using that, um, that that, um, you can see the path that it’s using. It’s going to that contact data. It’s going to that no bank link, um, custom attribute, and it’s saying, you know, if that bank link is false, then send them the second email. If it’s not false, then and then kick them out of the journey itself. So there’s another there’s a a simple way of how you can use that, uh, data from data cloud to, uh, create a different paths within Journey Builder. And, of course, you can do this in a lot more, uh, complicated way, but just wanted to show you that third part, which was how to how to use those data points directly from data cloud to do that.

Okay. So I know we’re getting close on time, um, but I did wanna cover the, um, on the data cloud side, the, um, the data action excuse me, the calculated insight in in the data action. So let me just quickly give you a couple of quick points on that, and we’re just gonna go quickly into the data action, uh, calculating insights to see how that’s, um, that’s created. So, um, a different way to populate data into, um, into marketing cloud using calculated insights and data data actions. The key part that we wanna cover here is the understanding of that, uh, unification model in data cloud. So you need to understand that model to pull out the data that you need to, um, send these subscribers using a data action. So you can see here in this in this schema, you you start with the individual. Right? I mean, that’s sort of like your contact or your lead in in data cloud. Now to get to the subscriber key, you have to link to the unified link individual object, and then you need to link it to the unified link contact point email. That’s that required contact point email in that, um, in that model that we pointed out earlier. That contact point email ID is the subscriber key. And then to get the email address, you have to do one more step, which is that unified link contact point email to get that email address to pull over into Marketing Cloud that, uh, allows you to send that email. All the other data points are coming from that unified individual. So it’s a multi object, um, scenario you’ll have to be familiar with, um, and sort of work through in your data to sort of see that come to life before you actually build those calculated insights.

So let’s just quickly cover in, um, in data cloud how you could create that that relationship, and I just wanna walk through an existing calculated insight. We don’t have time to create one from scratch, um, but just show you how this is created. It’s it’s a click not code solution, so you don’t have to be, um, a wiz at SQL, um, but you kinda need to know a little bit about SQL to to make this happen. So just like we walked through on that schema, these are the the three objects that we need to link to get to those data points. So we’re starting with a unified individual. When you start out afresh, there’s just this, uh, item on the canvas. There’s a plus sign. You can just add these joins. So if I click into this join, I’m looking to join that unified individual to that unified link individual. So each one of these joins you set up, you select, okay. I I wanna join the unified, uh, unified individual, and then you’ll be able to navigate to find the unified unified link individual. In this example, we wanna do a right join, uh, just so that the data waterfalls for each one of these join steps. And then you need to select the, uh, matching fields, the on statement that you would normally do on a from, uh, statement in a SQL query to get that data aligned. So that’s how you can, um, use, uh, those join statements to populate the data.

And then one last or two last steps. Once you’ve made that data relationship, you’ll want to select all of the fields that you wanna populate in the data extension, um, so that you can use that to send the email, to personalize it, etcetera. So this is where you’re gonna use this aggregate function. You’ll need to first create a unified individual count. Uh, we typically, in this example, use a deposit amount. It sort of gives you a total of the total deposit for this individual. And then you the dimensions, those are the fields and the date extension that you would wanna add as well. And then last but not least is the filter where in this example, we wanna filter those, uh, those people that have a deposited amount greater than zero. Right? We wanna find those people who’ve deposited money into, uh, into the, um, into the account. So that’s just sort of another way just to make sure that you’re filtering your segmentation.

Okay. Now we only got four minutes left. I just wanna cover one more area in, um, in in, uh, when you’re creating these data actions that’s very important. So whenever you’re, um, you first step is to create that that calculated insight. And, uh, once that’s created, you’re gonna have two data points that are gonna be really important. You’re gonna wanna note this is how you’re gonna align that data in Marketing Cloud. The first data point is this API name. So this is this gonna be the start of the date extension field that you’re gonna wanna create in the Marketing Cloud environment, this syntax. And then for every single one of those fields that you created, you wanna add to that syntax this API name in the data extension itself with an underscore separating these two data points. So it’s mandatory that you set up your data extension in marketing cloud with this exact syntax in order for the, uh, the calculated insight, the, uh, ultimately, the data action once it’s published to populate that data extension, uh, successfully. So if we go into, um, into marketing cloud, this is an example of the data extension that was created, and you can see let me see if I highlight this because it’s quite long. Um, you can see if I let me see if I copy this. You can see the data starts with that, um, the the data the calculated data, um, overall, um, see what it was called, a, uh, an API name for the overall calculated insight. And then you can see, um, after that, we do an underscore, and then it’s the individual fields that you wanna just paste that in. So that’s a little bit more specifics of how you can align these two data points.

Um, so we’ll we’ll wrap it up there. I, you know, apologize. I sort of ran out of time. Uh, let me just go back to my recap sheet. And, um, so hopefully, that gave you an overview of the use case of where this part, the data action part sits in the, uh, data the data cloud overall flow. Hopefully, give you a good deep dive into the segment, um, part of of the of how to create that end to end in the marketing cloud. And I do apologize running out of time on covering the the data action side. We do have a video which I I can drop into the chat that has this, um, this session. It’s about an hour and fifteen minutes going through every single one of these steps in detail, so please check out that YouTube video for this whole, uh, use case, sorry, for limitation of time, um, in in detail. Uh, and that’s that’s it. Um, did anybody have any questions?

Speaker 1: Hey, Tim. I’m live on stage with you now. We have no questions. Okay. Great. Actually, we did have one question. We can try answering it in twenty seconds here. Why are we creating segmentation for unified DMO and calculated insights? Why not leverage calculated insights for both?

Speaker 0: It’s a great point. You should explore both of those options, and you you’ll there’ll be different, uh, different ways to for using one or the other, but get familiar with both of those options. It can impact, uh, pricing, consumption, credits. Uh, so definitely explore both of those options. I think you’ll you’ll come to that answer by digging into both of those examples. I can’t just give you one answer for that, but it’s good to be familiar with both. Great question.

Speaker 1: Awesome. Alright, folks. That’s all we have time for. You’ll be pushed to the next session automatically. So enjoy the rest of your March, Remen, and don’t forget to join the closing keynote for our swag announcements. Take care, everyone.