Speaker 0: Hello, everyone. Welcome to today’s session, Demystifying B2BMA Plus I’m Heather from Sercante, and I’m very pleased to introduce you to our speaker, Jess Pine. So without further ado, Jess, take it away.

Speaker 1: Thank you. Um, hi. Uh, I’d say lovely to meet you all, but thank you all for joining. I am Jess Pine. I am a Lead Solution Engineer for Pardot at Salesforce here in The UK. So, uh, just just waiting to get started with my weekend here. Um, and I’ve been working with Pardot for, uh, for several years now. Um, and I’m really excited to be presenting on this topic today because every time there is a new, um, a new release around B2BMA and there’s more kind of Einstein, there’s more insights, there’s more more really good valuable insights that you can get from all the kind of automations and stuff. Um, I get really excited because I’m used to having to do all of this manually a long time ago. So really thrilled to be able to talk to you about this today and hopefully shed some light on on how to do some of these things. Um, so what we’re gonna go through today, uh, start off with a very quick overview of the differences between B2BMA and B2BMA Plus. Um, and then we’re gonna have a look at how we import and use data from other external systems, how we then bring that onto dashboards and customize them, um, and then how we would go through discovery stories and looking at predictions with Einstein Prediction Builder. So before we get started, um, I think we’ve got a quick poll. How, um, how many of you are familiar with B2B Marketing Analytics? Not necessarily Plus, but just B2B Marketing Analytics as a whole. Just looking for the answers there. Okay. So okay. Alright. This is good. This is good. So, um, we have some confident users. Welcome. Hopefully, we’ll see see some kind of things that you might not have seen before. But a lot of people who are less familiar, which is fantastic. Um, so I think we’ll be hopefully, what we go through today will be useful from kind of both perspectives, which is great. Okay. So, um, very quickly, differences between B2BMA and Plus. Um, so with B2BMA, you get the B2B Marketing Analytics app, which comes with five out of the box dashboards. Plus if you’re using advanced, um, and you have Einstein, and you also get an extra dashboard for the Einstein behavior scoring there as well. Um, and in all of these dashboards and and in the app itself, you can use Pardot and CRM data. Whereas with B2BMA Plus you have external data, you can bring in just data from external systems as well as Pardot and your CRM data. You’d have that B2BMA app that you have um, in the standard, um, edition, but you also have two additional apps which we’ll go through today, Marketing Campaign Intelligence and the Account Based Marketing app. Uh, and you also have access to Discovery Stories, um, and the Einstein Prediction Builder as well as a lot more data if you need a lot more data. Um, and I think the the the way that that I think about how the differences between B2BMA and B2BMA Plus, uh, is that B2BMA really shows you how your campaigns are performing. So you kind of analyze this. This is what this is what’s happening. This is this is how things are performing. Whereas B2BMA Plus gets a bit deeper and and helps you to understand the why. So so that’s how I like to think about it. Um, and what we’re gonna look at today, uh, we’re gonna look at from the perspective of answering the question, um, how can I increase license usage and reduce attrition? So we’ve got a fictional company that we’re that we’re kind of looking from the perspective of today who, um, they’re a software as a service business, and they, um, they sell licenses. And they’ve noticed that they’re they’re getting a lot of attrition. They’re not, um, they’re not getting as many renewals as they like, and they’ve noticed that some of their license usage is actually quite low in some accounts. So from an account perspective, we want to see which of our accounts have high and low, um, usage rates and then how we can use that data to kind of help improve those usage rates and hopefully hopefully improve renewals. So, um, to start off, we’re gonna have a look at how we would bring in that external data from from another system. So let’s dive into my wall and have a look at that. Um, so I’m in now Analytics Studio, uh, which is just just within, um, within the Salesforce platform. Um, hope that hopefully, this will be familiar to some of you, but it’s just an overview of kind of all of the, um, all of the different apps that I have, um, within within this. So I’ve got my my standard B2B Marketing Analytics app, which I’ve also got those B2BMA Plus apps. Um, and if I did want to use so if I’m bringing in data from other platforms, if I did want to use, um, external connectors, so, um, you know, maybe connectors to Google Analytics, maybe connectors to other marketing automation platforms, I can do that using Data Manager. There’s a way to manage all my connections there. But to just import a CSV, which is what I’ve done today or what we’re going to do, I’m just going to create a dataset, then you’ll see that I’ve got that CSV file there. Nice and simple. I’m not for the sake of time, I’m not gonna kind of go through that step by step. Um, but what we’ll have a look at is what I do next. So the data that I’ve bought in is brought in is this usage data here. So if we have a quick look at what that looks like, what we can see is I’ve got my I’ve got my account IDs. So I do have my account IDs in my external system. You need an ID to to kind of help help keep all that data together. Um, and then I have a breakdown of my monthly active users, my number of licenses, bought, and that monthly percentage usage there. This is the data that I’ll be using today. But of course at the moment, that doesn’t mean anything. I can’t put it on a dashboard because it’s not related to any of my datasets that I’m using in my dashboards. What I need to do before I do anything else is join it to another dataset. The one I’ve chosen here is my ABM opportunities dataset, uh, which is used very heavily in my account based marketing app. There’s a nice dashboard that results from that as well. What I’m doing is I’m joining these two datasets here. The important thing to know about joining these datasets is that we, let me just expand this, um, that I’m using that account ID as the join key. So that is what it is telling, um, that is what it’s telling my, uh, my recipe here that, um, that the data matches. Um, you know, I’ve got my, uh, my usage data against an account on that account ID and I’m just going to add it into this data table here, this dataset that already exists. Then I just need to set my output here. What I’m doing is I’ve set an output as a dataset um, and I’ve called that there ABM accounts with opportunities and usage. So I’ve tied all that together into a dataset that I can then use on my dashboards. Um, and then once I’m not Oh, you would click Save and Run here. I have already run it, so we’re gonna skip that. But what I’m gonna do is go back to Data Manager and just quickly show you the monitor page. Um, and this is where you can see all of your, um, all of your jobs, all of your data flows, etcetera, that are running at the moment or have run recently. Uh, you can see what’s run successfully and you can see if there are any issues that you need to look at. So it’s just a useful, useful place to be aware of. So, um, that’s fantastic. I know that that has all worked fine. So, um, that’s the good first step. I’ve got my data into the system. I’ve joined it in a way that makes sense for my app, um, and I can look at the next step. So just to recap, we had a lack of visibility around those usage levels because the data was in a different system. What we’ve done is we’ve just brought that data into B2B Marketing Analytics. A couple of points to remember, your dashboards, they’re only going to be as good as your data. So make sure that you’ve got, you know, data quality everywhere. Um, make sure that your data is clean. Make sure that you’re joining in an effective way. ID is always the best option if you do have that because that is typically the unique identifier, in a system. Think about what questions you’re answering. We know that we’re looking to answer questions about usage. That’s the data that we’ve brought in. Really think about don’t just bring all your data in and then start playing around with it. Think about what you need to know, um, and that will help you structure your data and bring it in in an effective way. We’ve looked at that external data, um, and now we’re going to have a look at how we customize our dashboards. So, um, how do we put that data on a dashboard? Um, and what I’ve got here is one I made earlier. So before we before we dive into that, though, let’s have a quick look at those, the dashboards that you get out of the box. So, um, with B2BMA, as mentioned, you get five dashboards out of the box, um, and this is one of the ones that you get with B2BMA Plus. So this is our Marketing Campaign Intelligence dashboard. Uh, and this is kind of an upgraded version of the, uh, multi-touch Attribution dashboard. You’ll see I’ve got some really great details here on campaign performance, on revenue and ROI from different campaigns, what that looks like at an account level. But my favorite bit about this particular dashboard is if we go to our campaign engagement tab here, we’ve got that breakdown of what that looks like over time. But really crucially, we’ve got asset engagement by job title. If you’re targeting a particular job role, maybe you’re targeting CFOs or marketing directors, you can see here exactly what is resonating with them. Here we’ve got CTOs, we can see that what types of content really works well with them. Clearly, they like file downloads, so they must like white papers perhaps. All of these valuable insights into the best ways to target those particular job roles. So I I I love that bit physically. Um, and then the one that we’re all going to be clicking more closely at is also our is our Account Based Marketing dashboard. So, um, this one here is really focused at that account level, you know, with with lots and lots of stuff happening in an account based marketing and and from an account based marketing strategy perspective and marketing at the moment. Um, so we can see here what we’ve got sales activities in the pipeline. We can see we’ve got a nice visual view here of our accounts and what’s happening at those accounts, where our top accounts are located regionally. As we’re starting to think about holding live events again, this can be super powerful when you’re thinking about locations for those. Um, and then of course what that looks like over time, as well as a breakdown of those opportunities by account as well. And any of these we can, you know, dive a bit deeper into and get a bit more insight into what’s happening at these particular accounts. So really valuable if you’re trying to analyze how things are happening at an account level. Um, which is exactly what we want to do here. We want to bring in our usage data onto this particular section of the dashboard, our account section, um, and, um, and add bring in that usage data so that we can analyze that at the same time. Um, the first thing we do, if we are editing an existing dashboard, if you remember nothing else, please remember this, is we copy this dashboard. We don’t want to edit a dashboard that’s already there. We want to make sure that we clone it, um, so we’ve got another version before we start playing around with it. Um, and that’s what I’ve done here. So you’ll see I’ve got Account Based Marketing Version Two here. Um, and what I’ve done is I’ve just brought in oops. Let me switch to the accounts tab. I’ve brought in, um, that usage data and I’ve got it into a nice table here, a nice chart where I can just switch between my top performing accounts from usage perspective and also my lowest performing accounts. So these are the ones, um, you know, these are the ones that I might be concerned about. I might want to think about targeting, um, you know, maybe a collaborative approach between sales and marketing to get that usage data up. So how have I done that? Um, the first thing you need to do is, um, click edit, um, and then you need to think about connecting your data sources. So even though we’ve brought in that, uh, that data and we’ve created the dataset, it’s not actually associated to this dashboard yet. So I need to go to my, um, connect data sources, and you’ll see here that I have connected my my usage data here with, um, with my opportunity data here. So it’s part of this dashboard. And if you look closely, you’ll notice that again, that is on that account ID. So I’m using that account ID across the broad across the board to relate. Um, and I’ve I’ve just dragged things around. If you haven’t tried playing around with dashboards and you do have access to B2BMA, do I do recommend it. It’s it’s pretty intuitive to drag things around, um, and you can you can build some nice things. I’m not gonna say this is the prettiest dashboard you’ve ever seen. My edition is obviously not the nicest, um, but I’m sure you can create something much nicer. Um, and all I’ve done here is I’ve got my my bars here, which is my Y axis, um, in my account names, and I’ve got my, um, percentage usage here in the, uh, in the access access. So just nice and simple using the data I’ve, um, I’ve already imported, and I just added it onto my dashboard dashboard there. And a top tip, if you’re editing a dashboard, you can just press your E key and it will toggle between preview or viewing the dashboard and editing. So there we go. That’s how I’ve brought that onto my dashboard there. Um, so quick recap on that. Um, so we were missing that usage data. We wanted to analyze that on an account level. So we’ve just pulled that in in a chart, uh, also known as a lens, uh, in in, um, in Tableau CRM, which is what B2BMA is built on. Um, and we so now we’ve got all of that data in one place. So we where it where it matters. Um, so a couple of things to remember, as I mentioned, always copy the dashboard before you edit. You don’t if you make changes, you can’t kind of revert back, so don’t so don’t do that. Um, and think first about your user experience. So who is going to be using this dashboard and what what are they looking for? What questions do they want answered? Um, and really kind of think about it from a UX perspective. Um, and remember as well, we didn’t look at it there, but there are lots of different options for charts. Charts are not equal, but it’s not all charts for all purposes. Different charts have, um, have different purposes and display the data in ways that make sense in different ways. So make sure that if you’re when you’re playing around with different chart types, that you’re choosing ones that make sense for what you want to display. Okay. Next section, um, Discovery Stories. So, um, at this point, um, you know, we’ve identified that there are those that some accounts that aren’t performing very well from a licensed usage perspective. Um, we’re thinking about ways to reengage them. Um, obviously, some of that will be on a sales team, but some of that might be from the marketing team as well. Um, so what we want to look at is how, um, how our marketing teams, um, how our prospects are engaging with our existing content and what in what ways we can actually improve that. So here we’re seeing, um, this is our one of our Einstein Discovery Stories. So what we’re looking at here is, um, our prospect engagement and how what types of things are influencing that. So we can see that, um, where activity count is the highest, people are downloading files. So people are downloading our white papers. We can also see how our business units are performing and what campaign types, etcetera, um, are performing well. Uh, but on this side from a low, um, low activity standpoint, you can see that actually our free trial campaign isn’t performing very well, um, and Form Pandas aren’t performing very well either. So so that just a little bit of interesting information. Um, but what’s really crucial and what can’t be done manually, easily, um, is the the fact that we’re combining multiple variables when we’re analyzing this data. So we’re not looking at, um, you know, we’re not looking at one of these factors in isolation. We’re looking at them together. Um, and it’s the same way if we want to start actually predicting. So if we’re looking here at, let’s say, we know our files are performing well, um, and I’m interested in what that would look like. So how how are how can I improve our file downloads from social media, for example? I know that I can change the campaign type, so I’m going to make that my actionable, um, attribute here. Um, and straight away, I get recommendations that actually if I switch that to general web marketing rather than trying to run, um, just run my white paper campaign on social media, I’ll see an increase of 41 activities, which is which is pretty substantial. So I’m going to do that. Um, and then if I analyze down here, I can see how those different suggestions and how those different factors are actually impacting, um, impacting that activity count there. So really, really helpful when you’re starting to predict and to choose ways to improve your strategy and your approach because you can predict the future. So from a discovery story perspective, uh, we’ve seen that that engagement was low. So we’ve identified different ways that we can predict different things that we can change that are predicted to improve that. So don’t neglect looking at those combinations and make sure that when you’re building out your stories, you’re selecting relevant fields. So you don’t want to be choosing things like phone number fields to include in your stories because that’s not gonna provide you with any valuable data. Okay. And then the final thing today, um, we’re gonna look at Einstein Prediction Builder. So, um, this here is, you know, we might have we’ve got some great insights, but we want to think about predicting the future and also what key insights can we draw from that. Um, and in this example, um, we’re looking at the the use case that we were we’re sending a lot of leads over that are actually being rejected by sales. They’re being marked as unqualified. So what can we do to, um, what can we do to improve this? Um, you know, why why are these leads being marked unqualified? How can we how can we identify who might be marked as unqualified and reject them? Also, what factors are impacting this? For this, I’m looking at my scorecard here or my Einstein prediction. I can see that I’ve got a really nice strong prediction here. I know that I’m predicting a yes/no value. Actually, the output of this is going to be a percentage likelihood to say how likely this lead is to be marked as unqualified. The really interesting thing are the predictors. If I dive into my predictions, I can see things like, um, if the company create the product created date has a big impact on whether or not this lead is unqualified. What does that mean? Was there a particular event perhaps that the way we where we imported a lot of these but but weren’t very good? And if we don’t have a company name, why are we handing over? Why are we assigning leads that don’t have a company name? So this this is, like, really strong, um, indicator for me that I need to fix something there. So whistle stop tour there. Um, but quick summary of what we just saw. So we saw we knew that a lot of leads are being marked as unqualified and we didn’t know why, so we’ve run that prediction to identify those causes. Um, and the output for a predict Einstein prediction can be a boolean, so yes or no, which ultimately is a percentage likelihood that something will happen or a number such as an opportunity amount, for example. Um, again, when you’re building that prediction, think about what data matters. So you might want to well, you will want to exclude phone numbers, or you might want to include sensitive data such as gender or anything else that might have a bias as well, Um, and make sure you’re not predicting the past. So, um, don’t don’t include data, um, you know, don’t include leads that have already qualified, um, that have already been unqualified in the data to be predicted because, you know, you’re obviously gonna get only one result there, which won’t help your predictions. Okay. So I have included I will I will share these with some great Trailhead links to get you started if you want to learn a bit more. Um, but I think now it’s time for questions.

Speaker 0: Alright. Thanks, Jess. That was amazing. Um, great content. Um, so a question of that came up, uh, with respect to the dashboards, um, the dashboards that you were showing on screen. Um, if if, uh, if if somebody’s not seeing those dashboards in their org, what needs to be done to turn those on?

Speaker 1: Okay. So if you have B2B Marketing Analytics Plus, so the dashboards that we looked at today would be B2BMA Plus. You will need to create, though, the apps the new app. So you’ll need to create the Marketing Intelligence app and the Account Based Marketing app in order to see those dashboards. And that that’s done in the same way as with the classic B2BMA app. And, otherwise, if you’re still having issues, raise a ticket. Does anyone else have questions?

Speaker 0: While we’re waiting for an OWA. Mike, yeah, you do have Pardot Plus Analytics Studio, so it would be a matter of just, um, uh, creating a new app, installing that. Oh.

Speaker 1: Alright. So that sorry. I I can see this coming through. So that might be, uh, it might also be a permissions. There are separate permissions for B2BMA Plus than with B2BMA. So reach out to your Salesforce admin and just check that you’ve got the right permissions there.

Speaker 0: Thank you. Okay. We have a new question from Ryan. If we want to measure, uh, the impact of campaigns with something other than, uh, dollar value, how would you suggest making those changes?

Speaker 1: What would you, um, well, I mean, you can you can edit the the dashboards to you would have to change, you know, perhaps manipulate the data if it doesn’t exist. I mean, do you have an example of what what you wanted to look at as opposed to dollar value? But, um, well, while while we’re waiting, essentially, if the if the data if that if the data you want already sits on your dashboard, um, remember when I had to connect that the the data on the dashboard as well as price of when I imported it. Um, you should be able to just change the charts that that are on the dashboard to to you to display that data. If the data isn’t on that dashboard, then you might have to connect to different sources and you might even have to create, um, create a new dataset to bring into that, um, that app if the data that you need doesn’t sit within the app. So it could be it could be a very quick one or it could be there are a couple more steps before you have the data you need. Oh, no. Sorry. I’ve seen that. Oh, yeah. Okay. Yes. So, um, if that data sits externally, um, then you would kind of go through the process that that we looked at today. And if that data doesn’t sit externally, if it does sit within Salesforce, um, you do there’s a you can follow a similar process, but you’re just putting the data in from a Salesforce object as opposed to importing it via a CSV.

Speaker 0: Great, thanks Jess. And, um, I think we’re at time now, so that concludes our session today. Um, I would like to, uh, um, give a shout out to our sponsors, uh, for their support for this session and, um, for everyone that has attended the sessions, um, so far. Um, so be sure to, um, uh, take a, uh, pop into our sponsor booths. And then also don’t forget to check out our closing keynote, which is coming up, uh, um, just shortly. Um, but other than that, thank you, Jess, and thank you everyone. And, um, enjoy the rest of the conference.

Speaker 1: Thank you.