Speaker 0: Alright. We got somebody from Chicago. Nice. Okay. Alright. Let’s get started. So we have an amazing speaker joining us today to discuss unlocking b two b MA, going beyond stock dash boards. Um, so with that, I’d like to introduce our speaker, Duncan McGovern from Petalucid.

Speaker 1: Alright. Thanks for the intro. We are gonna try to cover a fair bit during the, um, presentation today. And so the decks will be or the deck will be available afterwards, and there’s, um, a bunch of screenshots, and some of them I’m gonna move through pretty quickly, but they’ll be available for reference, um, down the road. So, um, without further ado, we’ll kinda dive in. Um, my name is Duncan. I live in Missoula, Montana. My background is in starting off kind of on the marketing side of things using Google Analytics, um, and, uh, looking at paid ads to try and kinda understand attribution, being part of a Salesforce migration, and working in house with Salesforce, still struggling with attribution challenges, and then, uh, kind of finally wind up winding up in the Pardot space start to connect some of those threads. Um, I’ve been running Peddle Lucid for about three and a half years now. We focus on engagement analytics, um, primarily with nonprofits and some, uh, b to b sales. Pretty much all clients are using Pardot and most are using some other BI tool in some fashion. So whether that’s b to b, MA, um, CRM analytics, Tableau, um, etcetera. That’s kinda our wheelhouse. The goal for today is to give a bit of an overview of CRM analytics, how b to b m a fits into that, and then diving into some specific examples. Um, most of the session is going to focus on the examples. I know there were a couple of other, uh, sessions around b to b m a kind of getting oriented, what the stock dashboards are, so we’re not going to linger, um, on that bit too much. And of course, uh, thanks to the sponsors for making this, uh, whole thing possible. Alright. So first, what is b two b m a? Um, it is an app that sits inside CRM analytics product, which was previously Wave, Einstein, Tableau CRM. Uh, the best way to think about an analytics app is that it’s kind of a workspace where you can lock down permissions, and so your data and your visualizations and everything can be app specific, um, and it’s it’s sort of one workspace. So you get one app with, uh, purchase of at least a Pardot plus tier and five licenses to go along with it. You got some preconfigured dashboards, um, but there’s also the ability to extend it. I think about it as a gateway BI tool. You can really start to explore your data without trying to save and load reports the same way as a Salesforce report. Um, so it’s a lot faster to explore much more powerful dashboards and really powerful ways of configuring your data. Today, we’re gonna really talk about things in in three steps, um, and the first two are gonna be, uh, fairly brief. So, again, most of the focus will be on the use cases. Um, first, thinking about your strategy and what you’re gonna do to prepare. Like, where do you wanna go and how is that gonna inform how you set it up, um, including kind of discussions with stakeholders about what you’re trying to get out of a BI tool. Second, a little bit of an orientation with CRM analytics, um, just the front end, back end, what to be paying attention to. And then we’re gonna look at three use cases. The first around, um, reconciling Salesforce leads and contacts with, uh, an account based marketing strategy. So how can we get a consistent view no matter what type of record someone is in Salesforce and how they progress through your sales pipeline, um, and what that resulted in? The second, more sophisticated campaign influence model, so building your own campaign influence model, um, without using Salesforce, uh, to do so. And then the last is to look at some other conversion points, um, that can provide a lot more flexibility in your reporting, uh, including, like, a demo request, thinking about that as a key conversion point rather than an opportunity being created, for example. So kind of starting in here with the prep and orientation. Um, I really am a big fan of drawing your data. This is in Miro, which is my favorite tool for it, but you can use anything you want. Um, but it facilitates having a discussion with everyone involved about what you’re trying to get and, um, what that looks like. This is an example, um, discussing how webinar sign up attribution is gonna be tracked and whether we wanna look at webinars as a channel, um, themselves for kinda one, uh, one thing in in attribution or looking at ways in which sign ups for RSVPing for a webinar across multiple channels, um, might be incorporated into that. And so kind of the model on the left looking at webinars as pushing, um, as being really what you’re attributing value to and then drilling down into that to look at the different, uh, channels of, like, paid digital versus email versus rolling those up in, uh, your attribution overall. So the visuals early on can really help in your data design. Um, so I think this is important to to think about. Moving on to kinda getting oriented with CRM analytics. Um, kind of on the front end here, this is the start of the user interface sort of consuming things. This is the analytics app. So you’re accessing, uh, CRM analytics through the Salesforce app launcher. Then within that b to b MA is an app within CRM analytics. It’s a collection of assets that you can share or keep locked down. A dashboard, a lot of parallels with the Salesforce dashboard. Uh, component, you can reuse little pieces of dashboards in other places. Um, a lens is analogous to a Salesforce report particular way of looking at one dataset, sort of one visualization. And then dataset is the the building block of everything, um, the data that’s going into it. On the back end, you can access all of this through the data manager, um, and what you’ll see over there on the left of the page, your jobs monitor, what’s going on in terms of processing data, the data assets, which are the datasets that you’re working with. Recipes, which we’re gonna talk some about today is how you prep the data for use and exploration. Um, usage is just company limits. Connections are the what you’re hooking up to to pull data in. And so for b to b m a, your real connection option is Salesforce with the full fledged CRM analytics tool. You can hook up to all sorts of different data warehouses, a lot of different places. So that’s kinda one of the limitations is you’re really restricted to Salesforce, um, and Pardot data. And data templates is just a wizard, um, for starting to build some of this stuff out. Um, so lots more to explore, but there’s plenty of resources for for digging into that. And, um, I want to start to dive into some of the use cases here. So the first one is, uh, leads and contacts contacts in an ABM context. Pardot sort of solves this for us in a limited way by, uh, just looking at prospects, but the out of this box dashboards don’t necessarily have everything that we wanna see, um, or in the way that we wanna see it. So I’m gonna walk through, like, building a leads and contacts dataset, um, and show you what that looks like, steps to do it, and then the benefits for, um, how you can what you can get out of that. So we’re gonna fire up a recipe here, um, and then select, uh, something to start with. So we’re picking leads, which is the data, um, source from coming from Salesforce. Uh, you may see that some of the fields aren’t in CRM analytics by default, um, which is that little gray icon. They will the next time the data is refreshed. Uh, kinda key concept here is that you need to to pull data into, um, Analytics in order to work with it on a scheduled basis so it’s not live. Next step, removing converted leads because we’re going to be adding contacts as well, and kind of our takeaway here is that the data is in more of a raw format, and so you need to specify things that you might not have to worry about in in other contexts of, um, um, kind of double counting leads and converted leads, and that would be using a filter here. When we’re adding contacts, we need to be aware of the fact that contacts and accounts are separate objects in Salesforce, and a join can help us pull that together to look at account values such as type or name. Um, so kind of another nuance here in the prep. And so we’ve got leads, we’ve got contacts with their account details, uh, and then we’re gonna use an append function to, um, join those two together. I I say join even though join is actually a different, um, kind of node and does something different, but, um, it’ll pull those two together by, like, adding more rows into your spreadsheet if you’re thinking about it that way. One of the cool things you can do is line up fields that don’t necessarily, um, have the same API name. Pardot will handle this for you, kind of company and account name, But, um, if you wanna line up other fields, you kinda have a ton of flexibility in how you align those two, um, datasets. Last example here or last kind of step is, um, we’re also gonna pull in opportunities. So looking at, um, excuse me, campaigns. So a first touch campaign, um, in Salesforce using this as an, uh, very similar to a Pardot, um, campaign where it’d be the first source of the person, um, but we need but we can pull that in here to this dataset. So it doesn’t matter if someone was added in through Salesforce. Um, we could see the that first campaign. That’d be a lookup on the lead and contact. And we’re also gonna pull in opportunity data to sort of see the outcome of of this. Um, so last step before we kind of take a overall view, um, cleaning it up, you can change the name of different columns here in using this transform node. Um, and one more thing to call out a little gotcha. By default, your analytics tool is gonna exclude blank fields when you start to filter on them. So there’s a setting that I recommend toggling on that will ensure that if there’s a blank value and you’re grouping by that in a visual, that they don’t, um, you don’t lose that that data. So a little bit of a call out, uh, on the Nuance here. Our final recipe, um, here we can see that we’ve got leads, contacts, pulling in campaign and account and opportunity data, um, making sure that we share it to the b to b marketing analytics app, not our private app, so it’s accessible by everyone. Um, and the result of this is that we get a dataset that we couldn’t get otherwise, and it can start to tell us the story of, like, what happened to marketing qualified leads from January. So here I’ve got a marketing qualified date that’s on both leads and contacts. So it doesn’t doesn’t matter which Salesforce object they are. We’re kind of date stamping that in Salesforce, and we get this view of how they were progressing, um, through to being worked by sales, if there was an opportunity, value of the opportunity, whether it was closed won. From this, there’s a lot of ways we can visualize it, um, but I just wanted to focus on the the data that you get here. And then, um, thinking about the, uh, dashboards is there’s a lot of potential here, and I’m I’m gonna get a little bit more into that in terms of campaign influence. The next examples, I’m not gonna spend quite as much time walking through the recipe. I know we’re moving pretty fast. Again, all of the slides will be accessible to help walk through some of these steps, But, uh, we’ll we’ll get more into visuals here in just a minute. Um, so here for campaign influence, um, the problem we’re trying to solve is that we’ve got a couple of out of the box attribution models with Pardot, um, but building a custom model is pretty challenging, requires some Salesforce automation, requires sales reps adding opportunity contact roles, which is certainly not a guaranteed thing. And so I wanna propose this alternative of building out a model in analytics and what that looks like, which is a lot more lightweight and can be really flexible. In this example, um, we’re gonna look at marketing touch points that are prior to the opportunity being created to think about what marketing activities were influencing pipeline creation. Um, and we’re also going to look at all contacts related to the account, not just those with, uh, opportunity contact roles. So thinking about how we’re gonna do this, um, a couple of key factors to keep in mind. Um, the first is only looking at campaign members with a responded status. So, um, there’s a great tool. Um, there are several tools that you can use to set up default campaign statuses based on, um, campaign type, but, uh, using the Respondent Indicator to look at campaign success is a really good way to to think about attribution. So you can have members of a campaign, but you’re not counting all of them. The next thing we wanna do is look at the difference between when the campaign member, um, responded and when the opportunity was created and exclude anyone who responded after the opportunity was created. So, again, this model is, um, the influence on pipeline creation. In order to break this out into different revenue, um, attribution shares, We are gonna divide the amount of the opportunity into, uh, all the different campaign members that we find, and there’s also gonna be a little tweak here where we are, uh, changing the weight of different campaigns to weight some touch points more heavily, and we’ll see that in just a minute. We can aggregate this at any level, whether it’s to the campaign itself, the parent campaign, the type of campaign, um, which we typically use that for marketing channels. And then in order to have a kind of a dynamic dashboard, this will create one dataset. We’ll add that to other, uh, influence. We can add that to, like, the stock campaign influence datasets and then use the filter on the dashboard to sort of switch between them. Um, that would be the same as if you’re setting up a Salesforce report using the different attribution models and using a filter to, um, switch between them. So, again, not gonna go through every step of the recipe, um, but this is how you would start to design that. So opportunity, adding on, uh, contacts and campaign members, filtering to only show responded campaign members, and only those that, uh, engaged before the opportunity was created. And then the next step is to split off the, um, data flow into kind of two tracks. One is to aggregate the campaign members um, at the kind of to the opportunity ID and, uh, a little bit of a tweak to update field names, join them back together, and then calculate the revenue share. And this next slide will help kind of explain a little bit more, um, what that looks like. So if we’re thinking back to, um, the recipe here, this is the dataset that is being generated. We have highlighted a couple of opportunity IDs to, um, sort of show really clearly that each each of these opportunities has a couple of different um, engaged campaign members. That’s the responded status. The amount of the opportunity is we’ve got one that’s 260 k, one that’s 50 k. The aggregation that we’re doing, um, on the last step here on this slide, so account of campaign members, what we’re actually doing is summing up the weighted engagement value, and that’s being pulled in from, uh, the campaign. It’s based on campaign type. So this is a model that was, uh, loosely based on Engagio’s, um, minutes of engagement model where, uh, an email open is maybe two minutes of attention, whereas a partner referral is an an order of magnitude greater at twenty minutes of attention, paid search, and direct mail, uh, kind of in the middle there at ten. So this is optional sort of bonus going above and beyond, um, um, different ways of just looking at an even revenue share, but ways to, uh, kind of slice out the different types of marketing channels for, uh, for how that could work. We arrive at the revenue share by dividing the amount um, by the weighted engagement, sum, and then multiplying by weighted weighted engagement. So each of these kinda comes up with a revenue share. And when we put all this together, we can do something really interesting with it or lots of really interesting things. So coming up with a dashboard like this where, um, you can see kind of in the middle there where we can see that this is the influence model that we’ve selected. And so feeding into this, um, dashboard are a whole bunch of other influence models as well, but we’re just choosing to look at this weighted one. Um, and this we can pull up average number of touch points, um, revenue share across time. We could switch to look at a different revenue share, like a first touch or last touch or an even touch, and then get some more metrics down at the bottom of, like, campaign type, campaign name, uh, total ROI, etcetera, but all based on this choosing how to look at the, um, look at the data in a different way, and it doesn’t require any Salesforce automation to get here. Last example, looking at some other types of conversions. Um, and so the, uh, first one I wanna show off is looking at engagement prior to a form submission. So rather than using the creation of an opportunity, um, for your your goal. Um, this would be submitting a demo form, contact us form, pricing request, etcetera. This view is a lens, which is not a dashboard. It’s that report, um, kinda concept that I talked about, and this could be added to a dashboard. The cool thing about this is you can add filters and change your groupings without having to sort of save and run it out, um, run it through again. Um, and so without adding automation to Salesforce, we can start to look at, like, what were all of our, uh, marketing campaign engagement channels prior to submitting different types of forms here. Last example, um, kind of building out more of a dashboard to help tell this story. So this is using something very similar to that very first dataset of, like, leads, contacts, and opportunities, um, and pulling that into a dashboard. So here, we’re looking at, at the very bottom, volume of prospects, next step up. Um, this is our account of marketing qualified records, whether it’s a lead or a contact, so kind of a really flexible way to to do that, uh, number of meetings booked. So, again, kind of thinking about this demo request as as a key conversion point, uh, and then pipeline. I know we’re getting a little tight on time, so I’ll kinda run through this quickly. But, uh, one more feature to highlight is that hovering over can get you some drill down. So just hovering your mouse over a certain month, we can start to break that down, um, by a different campaign, which is pretty cool. Um, here this is a little bit of a summary of, uh, what what we just did in that last chart, um, and two heads up, uh, things to be pay attention to. Just being aware of as your the way you’re pulling your data together gets more and more sophisticated, um, being really clear on what those things mean. And so, uh, on this chart, our months at the bottom, that would be the date that the prospect was created, not necessarily the date the opportunity was created if we’re looking at the green line. So making sure that everyone’s really clear on what what story is being told. Um, and then the last piece just to be careful about how you aggregate data because it it will not dedupe the same way that Salesforce will. So Salesforce, if you have five contact roles and one opportunity and you run an opportunity contact role report, your sum of the opportunity amount is just gonna show up as the value of the opportunity. And in analytics, I think this is one place where you can get, uh, kinda get into trouble of having that amount, um, kinda show up multiple times. So takeaways for today, um, really powerful tool. Most folks using Pardot have access to it. It’s an awesome tool for small teams, build proof of concept, really start to explore what a BI can tool can do. Downsides, um, and some gotchas. I talked a little bit about the aggregations, making sure your null values, um, are handled correctly. The main thing to be aware of is that the licensing doesn’t really scale. So you get five licenses, and after that, you’ve got to purchase a full CRM analytics license, which gets quite expensive. So if that fits your larger analytics strategy, awesome. If not, I still think it’s a great way to start to dig into this, uh, and then consider, uh, shifting to a different tool or, um, kinda keeping it limited to a small team. But Salesforce will not sell individual b to b MA licenses, um, as of the last time I talked to them, which was a couple months ago, which is kind of unfortunate. Um, so thank you all so much. I know we covered a lot in this session. There’s my email, um, if you wanna reach out, or you can find me on LinkedIn. Um, I hope that the slides will be helpful, uh, in going back and looking at specifics, um, too. So I tried to include a lot for for context. So thank you all.

Speaker 0: Thank you, Duncan. That was an amazing session. Unfortunately, it looks like we are out of time for, uh, q and a. However, um, if you do have any questions, please reach out to Duncan directly through the event chat. So we have a bunch of great sessions coming up in a few minutes, and head over to the agenda to check out the full list.