Accelerate B2B Growth with Actionable Insights - Unlock the Power of Customer Journey Analytics B2B Edition

Transcript

Hey everybody, welcome to Experience League Live. Glad to have you here today. My name is Doug. I’ll be the host today. We got a couple of great guests. We got a great topic and looking forward to the show today. And the topic by the way, in case you hadn’t read it and you’re just in here because you’re fans of the show, I don’t know. It’s Accelerate B2B Growth with Actionable Insights, Unlocking the Power of Customer Journey Analytics B2B Editions. So we’re really psyched to hear about CJA B2B and how it can help you with this special use case of, of course, business to business. So we are going to get our guests in here in just a second. I am going to first tell you that we do have, you may have seen this in your registration thank you email. We have a demo environment. I see Sandro’s put that in the comments. So that’s awesome. If you haven’t already, you can click there. I also have the link down in the description on YouTube. But you can click there and you can sign up and it’ll get you a click, a link into a demo environment and we will have you, thank you. There we go. And we will have that available to you during the show. So you can play along. I will say however, that we’re not going to like stop the show to you know, if you have any issues where you’re trying to get to the right place or you’re not caught up or those kinds of things. We have people manning the chat. So if you do have a problem accessing that, then go ahead and put that in the chat and let us know if you’re having an issue there. And then we’ll just kind of forge onward here in the show. Anyway, hopefully this works great for everybody. Anyway, without further ado, let’s get our awesome guests in. First, we will welcome Caitlin Vonneg. Caitlin, welcome to the show. Come on in. All right. Glad to have you. Our studio audience is slow. They really wanted to, you know, make sure you were there. And our other guest today is Kerry Olson. Come on into the show. Kerry. All right. We’re waking up. Yeah. All right. That was a good crowd. Yeah, they were ready there. All right. Thanks you guys. Appreciate you coming in and being our experts today. We’re going to have a good time and get some good information to these folks about CJAB2B. Before we jump into the topic, I want to let the folks kind of get to know you guys a little bit so they know where you’re coming from and what you do all day and that kind of thing. So Caitlin, let’s start with you. You’re a digital marketing technical evangelist.

Is that right? Yeah. Say that five times fast. Yeah, exactly. What is an evangelist besides evangelize? How can you tell us what you do all day? There you go. We evangelize our products. So I work really closely with our PMs and our PMMs to make sure we understand the latest and greatest of what’s coming on the roadmap. How can we tell those future stories as well as what’s coming to market and help stitch together all of our products to show the power of the platform at events like Summit or Experience League Live and really just get the magic out there for our customers and help them see value in our products.

Nice. Thank you. Yeah. And Cara, you’re a senior expert solutions consultant. So yeah, you’re down and you’re in the weeds there. Taking all those questions and things like that, right? Tell us a little bit more about your involvement there. Yeah. So I’m a senior expert solution consultant with Data and Insights. So that really just means I focus all of my time and energy on customer journey analytics and talking to customers about their challenges and helping them see how customer journey analytics could address some of that. And in particular, I work with a lot of high tech and B2B companies. So really excited to share the power of what B2B edition unlocks in these new and complex use cases. Yeah. Yeah. Great. Awesome. And again, before we move on, first, we have to know a little bit more about you guys. Like we saw in the lead-ins video there that, Caitlin, your favorite hobby is making homemade pizza or pasta and pizza, I guess. You just came along. When did you start doing this and do you have Italian roots? I do not, but my husband does. And so we perfected generations down ravioli recipe from his Asnona. And then in COVID, of course, we perfected our sourdough pizza crust recipe. Oh, nice.

Yeah. Very nice. Okay. I love it. I love it. And Kerry, you know, Cribbage, I mean, I’m embarrassed to say, I don’t know that I’ve ever actually even played Cribbage. That’s not, yeah, I’m not asking for a board here or anything, but no, you got into it and you’re like, I can make those and do those for gifts. And so… Yes. And someday if you see me around, you can ask me how Cribbage helped me get my job at Adobe. Oh, okay. Cliffhanger. Okay. You’re a Cliffhanger for the next show. Yeah. You got any of those coming up for this Christmas for any friends or family? I do. I have a special one. I had some friends get married last year and I preserved their flowers and making a Cribbage board for them. Nice. Very nice. Okay. Love it. Well, thanks guys. Thanks for being here today. Let’s dive in. So Caitlin, I think you’re going to kind of start us off, right? And kind of give us the background, kind of give us a frame of reference here before we dive into a demo.

Sure I am. Okay. Let’s take a look at that.

All right. Thanks, Doug. So yes, so Carrie’s going to jump into the fun stuff, take you through a guided demo, but I really just wanted to take a quick step back, set the stage. Some of you might be familiar with Customer Journey Analytics. Wanted to take a moment to kind of help you understand how this is different with B2B edition and kind of what the macro trends in B2B space are and why and what led us to build the B2B edition here at Adobe. So we know with Customer Journey Analytics, that’s one place that really helps you analyze and visualize all that customer data across one platform, both online and offline touch points coming into that one place, your kind of data command center. But the thing is it’s at the individual level. And while that works really well for B2C, what we’re finding and some of the trends I’ll talk to for B2B is that we really need all of that data again, online, offline, multiple places all in one place. But of course, at the individual level, in addition to what the B2B edition, three new entities, the buying group, the opportunity and the account level as well. And so I’ll take a quick step back and just look at the B2B market in general and some of the key challenges that really led us to put our heads down and figure out how we could build something that would really help in marketing have that one kind of view of data that would help them work towards those shared KPIs. And the first is really those cross channel customer experiences. I’m sure we all are very aware of, of course, increasingly now with multiple channels, a lot of our customers are actually I think upwards of six, they’ve done upward of 60% of their research on a product before they oftentimes even engage with sales. So that’s a lot of data that we need to capture and understand and factor into our go to market. The second one is increasing technology debt. There’s a lot of tech out there, B2B companies in particular, you know, we don’t want that shelfware there. So what can we do to minimize all of that tech have the one solution that really works for us? Third, we know it, we love it. It’s everywhere. AI, B2B companies are also expecting now the agentic and the autonomous capabilities to help us drive those intelligent decision making capabilities. And you’re going to see today actually, Carrie talk about data insights agent as well as our generative AI captions in CJB2B. So you’ll see first and up and close and personal with how we’re handling AI with B2B edition. And then lastly, of course, we always want to look at, you know, our cost, you know, what does it cost to retain a customer versus attracting that net new customer? And how can we better retain those customers where it is that lower cost. So now that we’ve looked at kind of those macro trends on top of that, we also know that the buying journey is within B2B are getting a lot more complex, we have a lot more decision makers within a buying group within an account, it’s harder to keep track of them, we have multiple different channels, lots of different interactions, lots of different places, we can find information, LLM, etc. And so this is making it a lot more challenging, a lot more tools, a lot more tech that’s involved in that process. So this leads us to say, you know, what do our customers need, what’s keeping our customers up at night. And a lot of what we’ve heard from our customers is oftentimes, both sales and marketing don’t have that one view, or those shared KPIs, or that shared data view and language around one, their shared KPIs to, you know, oftentimes, they don’t know where their sales are, where their accounts are within that sales cycle, from both perspectives, marketing and sales. And oftentimes, with this introduction of the concept of buying groups, we often don’t know who that buying group is, again, with the view of marketing and sales in that account.

And so there’s these, when we look at B2B companies really trying to deliver an impactful go to market, we see, oftentimes, companies are falling a little bit flat due to the lack of, you know, sales and marketing not knowing entirely what’s working today through kind of these four different areas. And so even though we might be qualifying leads with marketing, we don’t know if that’s, you know, a valuable lead or not. So how can we look at that customer lifecycle journey across all of the lifecycle and really understand, you know, powered by that cleaner data model with B2B edition, how can we look at the data that we need to drive those more unique customer experiences. And so simplify this even down further to just two main points. Essentially, coming from our own customers, our own customer advisory board, it’s these two things. One, I need to bring all that different data across from all my different systems into one cohesive journey that I can see. And then two, how do I do, and I’ve touched on this multiple times already, how do I get sales and marketing using that same data, talking about the same analytics data or language so that we can move that revenue, move folks through the pipeline farther. So that led us to look at what is that gap? What is that shared analytics gap across the ecosystem currently, where we might be able to help move that needle? And so we looked at ABMs, we looked at marketing automation platforms, ABMs, account-based marketing platforms, and then our data visual visualization, our BI tools. And we know ABMs are really strong at things like where that pipeline influenced by paid media or intent data or advertising channels, but what we’re really lacking is that end-to-end or the behavioral insights as well. For marketing automation platforms, it’s great. We’re connected to CRMs. We have those third-party integrations and we understand how we can personalize those messages, but we don’t often have it at the account or the buying group level. And then finally, our data visualization. I know probably a lot of you on here love those tools. So we have other tools out, or there are other tools in the ecosystem, but again, they’re not visualizing things in an easy way across the entire life cycle. So that’s really the differentiator that we wanted to solve. So right there in the middle, you’re going to see how we at Adobe decided to kind of fill in the tool and create a product that fills that analytics gap. So number one, it’s really what, like we said, that complete holistic detailed view of the entire customer life cycle. Two, making sure that we have that accessible data across the stages to actually take actionable insights. Three, again, shared KPIs, shared vision of the data, how do we move together faster? And then finally, making sure that comprehensive lead to revenue marketing performance, we can actually see that and report on it. So drum roll, that is what led us to create CAA B2B edition. And so this is purpose-built, very unique in the industry. Let’s see if we can get it to load here. All right. I will speak to this slide. It’s purpose-built. It’s an application that provides that organized and chronological view, again, across the entire B2B customer life cycle journey. And we know from talking to customers that we want to synchronize data, synchronize their actions from achieving shared account-based marketing KPIs. Talked on that. Number two, we want to see those shared revenue goals so that we can both, sales and marketing, drive account acquisition, growth, and retention. And three, we want to build that product value and adoption. And I’ll speak to these three use cases in just a second. And just to further highlight the differences between CGA and CGA B2B edition, this is how it might manifest itself in a dashboard. And Carrie will go through this further. On the left-hand side, we see things like people, orders, products, views, which makes sense in the B2C space. However, at the B2B space, now you’re seeing how do we see that deal size change, the accounts, opportunities, what buying groups, and then you compare those and measure those against one another. And then underneath that, the architecture beneath that is different between the two, giving us the power on the B2B edition with the account and the buying groups as well. And then finally, before I hand it over to Carrie, here are really kind of like the three main use cases. And Carrie will touch on some of these. I touched on these earlier. One, optimizing that account-based marketing. So knowing who in the buying group so that we can personalize that content, what roles within the buying group are actually responding well to the content that we’re serving them across the campaigns. For growing key accounts, being able to see a comprehensive view of the sales cycle. Where are people falling out? Where are accounts falling out of that sales cycle? How can we identify and unstick any of those touchpoints? And then also, how do we identify those high-value touchpoints? You know, what is working? How do we do more of that? And then finally, and ultimately, the goal is, of course, shortening that sales cycle, getting to revenue or a closed deal faster. And we can do this with those shared, that shared data view and having that one single source of truth. So that’s a little bit about, you know, customer journey analytics that we know and love. How do we add more magic to be able to do that in the B2B space? And then finally, now we’re actually going to pass that off to Carrie so you can dive into the demo.

I just want to, I just want to remind everybody that, you know, there is the chat there. Feel free to put any questions that you have, you know, in there so that we can, we’ve got the experts here that we can have answer your questions. So today, you know, it’s an ask me anything. You know, so feel free to let us know we can address those.

Boom, the demo. The demo company would like to thank the band leader. Thank you.

Thanks, Caitlin. Thanks, Doug. So like you mentioned, you are absolutely welcome to follow along. If you are wondering where to log in, you can always go to experience.adobe.com and log in with the email you used to register. If you already have credentials, you’ll want to make sure that you have switched to the Adobe Analytics University Student Sandbox, because that’s where we’ll be going from. And you’ll want to make sure you open the project once you’re in customer journey analytics, the one that’s called Experience Lead Live, CJAB2B edition. Now, if you are having problems logging in or you fall behind, don’t worry, you’re getting this recording and you’ll keep access to this environment. So you can come in, play along. We left a lot of notes. You’ll see I’m not going to read all these notes, but they’re in here for you to be able to digest what we go through, right? So follow along, play along, or just listen and follow along later. Yeah, that’s probably, that’s probably one thing that I would do is probably just watch and try to get on my own later on. So thank you, Keri. That’s great that they’re going to have access to that after this, during the recording as well.

Excellent. So like Caelan mentioned, with the B2B world, right, we are interacting across so many different channels and we’re collecting a bunch in web and mobile web. And maybe we have app, we have our sales meetings, we have customer satisfaction, we have webinars, right? We have in-person events. We need to be able to see all these different touch points that help not only just in the pre-purchase, but what about during purchase and post-purchase so we can understand all the people at the account, how the account is performing, how we’re moving opportunities along, how are buying groups engaging? Are they interacting with what we’re showing them? How can we look at cross-sell and upsell? That means B2B questions are really hard to answer. So we needed a way to bring this all together with those relationships. And that’s really what B2B edition does because we’ve unlocked the ability to get really granular at the account level, at the opportunity level, at the buying group level, at the person level, right? So we’re really opening all those doors to really unlock all of that. So you’ll see here, we’ve got some great data sources. We’ve got a lot of different events and sessions and people, but we can start asking questions like, okay, what are our highest revenue accounts, right? If you were previously maybe an Adobe Analytics user, you didn’t necessarily have that sort of information because it lived in your CRM, right? And maybe you want to understand your leads generated and maybe you want to understand how that’s trending with revenue closed one. But again, that lived in our CRM, so it was really hard to understand activity and engagement to what’s happening with the lead. So maybe some of you are familiar with Customer Journey Analytics. Maybe you haven’t seen Customer Journey Analytics at all, and maybe you’ve lived in Adobe Analytics. We’re going to talk about kind of some differences with Customer Journey Analytics and Adobe Analytics in the B2B context and also do some exercises. If you’re new to CJA, don’t worry. That’s totally fine too. We’re going to kind of walk through it at a comfortable pace, but we might be moving along kind of quickly. So if you are new, right, we’ve got all of our dimensions, all of our metrics. This is demo data. So if you see something in here, you’re like, what is this? Well, it’s just our demo environment. This is always going to be reflective of your data when you come to stand it up. What I want to do now in particular is go, okay, if I want to understand my leads generated, what is the lag effort to our revenue closed one? So maybe I can start understanding that sales cycle. So we’re going to visualize not just in a table, but what are these generated to revenue closed one looks like. So I’m going to come over to my visuals on the left and I want to grab my combo chart and you can just drag it over. Everything in Customer Journey Analytics is drag and drop. From there, what we’re going to select is month. I want to look at this by our most recent months in my visual and I want to take a look at my revenue closed one. And you know what? I want to see if I can maybe understand different elements here. And in this case, I’m going to compare it to a secondary metric and that’s going to be my lead generated. And what I like about showing you this is you’re going to notice as soon as we build it out, it’s really kind of hard to tell the lead generator just looks flat. So this is my first tip to you for the day. Whenever you’re looking at a visual, you can come up to the cog and there’s a bunch of different settings you can take a look at. In this case, I’m going to click on normalization. So it will normalize my leads generated, which is a much smaller number than my revenue. And we can in fact see, okay, we do generate a lot more leads and the lag time for revenue closed one is pretty significant, right? And we are starting to see a little bit of a drop off. So we might want to start targeting some of our lead generation, right? We want to might drive some marketing efforts to that because we really know that we have closed one as a lag effect of leads generated. Now, again, if you’re new to customer journey analytics, let’s talk about some ease of use features. So like Caitlin mentioned, we have a lot of power here with customer journey analytics to get to these self serve insights, right? So we want to turn these complex data sources into a really easy to use interface. So with customer journey analytics, we have something first called templates. So if you’re in here with me, you’re just going to navigate back to workspace. I already have my tab open over here. And you can click on templates. And you’ll want to make sure you’re in our CJA B2B edition demo view. But what I wanted to show you is there’s a bunch of prebuilt templates. Maybe you’re in here and you’re like, okay, we have our data in, I really don’t know where to start. Well, maybe we want to take a look at opportunities. Or maybe let’s actually look at buying groups, right? I want to see buying group activity. I don’t even know the questions I want to ask about my buying groups, help me visualize this in some way. So you can always click use template. And in this case, I’ve already run it ahead of time. But it auto generates this dashboard for us, right? Where we can go, okay, in the last three months, we see we have about 4000 buying groups across 2500 accounts, right? That’s accounting for 12,000 people. That’s pretty great. So we can understand maybe the different engagement they’re experiencing. So the events, how many sessions these specific buying groups are having, that could help us maybe target buying groups that are under engaging right, that we might want to re engage with, we might want to see the specific people within a buying group. So here we’ve got our filter for specific buying groups or specific accounts. And then we can actually drill into information about the people right. So this is pulling from my pseudo CRM ID. Again, a great way to just kind of get started and understanding buying groups from there, of course, is always the next question, in which case now maybe we’re going, okay, we talked a little bit about lead generated, maybe I want to understand that revenue closed one. And I also want to understand what about those accounts we’ve lost, right? So revenue closed, lost customer journey analytics, and this is for folks who are familiar with Adobe Analytics. This is a new feature for customer journey analytics called guided analysis. With guided analysis, we can ask a variety of questions about our data, maybe you’re not a technical user, maybe you don’t know where to start. And maybe you want to see things like active growth, where you can always see users who are engaging, who are new or maybe returning, you can always understand what these visuals are by just clicking this little I. One of my favorites is release impact. So maybe if you’ve made changes to say your website to help drive engagement to webinars and you want to see did we get more webinar registrations, you could use something like release impact. For today, we’re just going to stick with trends. It’s a nice, easy one. I’m going to drag trends over and it will pop up here and it allows us to go, okay, I don’t know how to engage with analytics, I’m not sure what’s happening, we’re going to create a trend here. It pops up this nice little visual and I’m going to say trend me my revenue closed won. And I actually also want to say trend my revenue closed lost. So all this is doing is giving us an even cleaner interface for different elements here. And of course, we can take a look at this as counted by users, but we could maybe take a look at all the different events. In this case, I want to keep it specific to users. Maybe we want to look at specific segments, maybe our high affinity accounts or like we looked at buying groups that weren’t engaging. Maybe we want to understand buying groups as well. So we create a segment of that. The nice thing is this visual also gives us a little summary of what we’re looking at, right? So we can see that revenue closed won over the last 30 days represents about 22% of users. So there’s always great ways to get insights from these visuals as we’re I’m going to go ahead and cancel out of here. We don’t need to save it, but I just wanted you to get a sense of some of the ways we can unlock some easy access insights. And then finally, okay. Oh, was there a question? Can I jump in? This is just me asking, nobody’s asking this one, but I have questions. But I guess I just want to say, first of all, that in case anybody’s kind of wondering, this is all kind of based on the information that they’re bringing in. I know it’s probably beyond the scope of this call to kind of talk about the data connections and the data that you’re bringing in, but maybe kind of talk about maybe what is the data kind of backbone to these kinds of great visualizations that you’re showing here? Yeah, that’s a great question. And honestly, where we spend a lot of our time with customer journey analytics, we are powered by Adobe Experience Platform, which allows us to really ingest from a variety of data sources. So when we start talking about the power of bringing that data in, we can connect to whatever CRM you’re using, whatever marketing automation tools you’re using, right? It’s meant to be agnostic and really help power those insights. Now with customer journey analytics, what makes it really special and impactful is that instead of just being a visualization layer, it’s really a business intelligence layer. It’s meant to put those data sources in sequential order and allows us to pivot it at multiple granularities. That’s the really cool thing about B2B edition is instead of us pivoting at the person level, we can pivot it at the account level or person level or buying group, right? The thing with B2B questions is they’re so challenging because we’re constantly needing to look at that data that way. And because we’re powered by customer journey analytics, we can do things non-destructively and retroactively to our data. So when we’re starting to look at attribution, for example, which we’ll look at here next, we can unlock the power of saying, well, I don’t care about last touch. I want to know how this played out in multiple touch points across this whole journey. And you don’t have to worry about realigning the data or resequencing or rejoining. It’s run at report time and non-destructive. And we can go, okay, that was great for account. Now let’s do it at the buying group level. And we get it in seconds instead of days that it would take to rejoin and reclassify this data. Yeah, nice. Great question. And I love that if we’re going to talk about AI assistant, which the reason this is so powerful is because we’ve done all that data standardization. Now we can start to unlock some of our agentic capabilities. So in customer journey analytics up here at the top right, you’ll see AI assistant, and you can start to ask it questions like, I’m going to say, how many leads … Let’s make sure I type it in correctly. How many leads were generated in the last month? Let’s do the last 90 days. Because maybe I want to … We started asking questions about leads to revenue closed one. Maybe I don’t even know how to drag that in. I just need something to be visualized for me. It auto adds this visual and goes, okay, 323 leads were generated. And that’s great. That’s helpful. That’s a lovely summary. But you know what? Actually break it down by account for me.

And this is just a nice way to go, okay, I got that first question. Now I need to understand maybe our account. And so we’ll see a new bar graph appear here where it’s going to show us the different accounts. But I like to see this little table here where we can see, okay, we’ve had some high performing accounts this past month. Maybe we want to make sure we get them into some special activation or nurture campaigns. So we’re really able to already gain that to some very actionable insights just by asking a few questions and getting this visualization auto stood up for me. And again, with customer journey analytics, this is my second big tip. Whenever you’re like, what else can I do? If you right click on a visual, you’ll get a lot of different options. And in this case, I’m looking at these four accounts who have four leads. I might want to make sure that we have a special campaign for them. Like I said, a nurture campaign. We segment or an audience from these selections to send downstream to CDP or to Marketo Engage to maybe do specific campaigns for our newly engaged leads. So we have a lot of interoperability here to kind of understand what’s happened. Yeah, I like that. As you have it, if you go back, sorry, I was just going to kind of show also that in case people were wondering about that, it was trying to show what, you know, like 100 different accounts, right? And so it was just a nightmare trying to label that under the graph. And so as you selected those top ones, you guys might have seen that it just changed the graph. So it was only those. So if you do want to see just those, you can just highlight those and lock it or you can say, well, I don’t need 100 of them. Just show me the top five or whatever. Right? So you can do that. Yeah, you’re a super user. I love it.

Look, I’m here. I need help. But I was seeing that and I’m like, yeah, my head started to explode trying to see what was going on with all of those accounts at the same time.

Yes. So a couple other elements here of our AI functionality. If you’re a familiar Adobe analytics user, you’re probably familiar with our anomaly detection. In this case, I’m looking at trended leads over the past 90 days and we can see, you know, it kind of gives me what it expects. And we do have one anomaly. We had a really effective day. Maybe we want to understand what marketing efforts helped lead to that. Did we have some webinars that day? Right. Seeing that kind of anomaly could help us know where to dig into our data. But we also have forecasting, right? So we does look like the rest of my month of October, it’s forecasting lead generation. We really want to make sure that maybe we’re doing some retargeting of our leads. We’re seeing that fallout. We’ve seen it a couple times now. And then finally, Caitlin mentioned this, anytime you see this little square in a visual, it’s going to be intelligent captions. And what I love about this is it’s going to give a natural language summary of what we’re looking at, right? So we can see that there’s a lot of variance in some of our lead generated. We can click through and it will tell us, hey, this was our lowest leads. It looks like the 17th. We got nothing done. Maybe we want to understand what was happening. Maybe we just had our data entry person out that day. Right. We can understand and take a look at what’s happening. And it really kind of helps guide and focus us when we’re looking at this data going, what do I ask next? What do I need to do to understand what I’m looking at? So there’s a lot of great AI and functionality within customer journey analytics really meant to kind of unlock the ability to, one, ask questions and two, understand what you’re seeing. All right. Next one I want to talk about is attribution at the right granularity. What I like about this, and this is really to me, if you are familiar with CJA, even if you’re not, B2B edition is really going to unlock what we’ve needed so long in the B2B world, which is the ability to understand marketing efforts to the account, right? So we’re going to take a look at that in action. And so you heard Caitlin kind of talk about these new entities, right? B2B edition really unlocks our ability to say, okay, we have everything on the account level. We also have these buy-in group IDs. We also have these opportunity IDs. We need to be able to switch the level that we’re looking at to understand different efforts towards that. So I have a free form table right here that I’ve pulled up already. What I want you to do is grab your marketing channel and drag it over. And like we said, everything is drag and drop. This is great. It defaults to the number of events. What I want to actually do here is go show me lead generated. And what I love about doing this, doing about this, is you’ll notice right away it all drops to no value. And that’s like, oh, that’s odd. But of course it makes sense. Our marketing channels don’t know a lead was generated. Leads are generated in our CRM, right? That’s where they’re stored. So with customer journey analytics, what we can do is look across data sources, look across channels and attribute events that happen in different channels to events in other channels. So at any time you can, like I said, right click. And in this case, we want to modify the attribution model. And we have a bunch of rule-based models within here. Maybe in this case, I want to know, show me all the marketing channels that at least participated in the lead being generated. And with B2B edition, now instead of that person, this is where we can unlock and reclassify to ask those right questions, right? So maybe I want my global account. In this case, I want my account, right? I want to know how did these marketing channels generate leads for my account. And of course, like Galen mentioned, our deal cycles take a lot longer. So we actually want to look back, not just 30 days, we want to look back 13 months. Show me the marketing efforts in the past 13 months that helped generate this lead. And like we said, this is all run at report time. So it automatically updates and we do see, okay, great. Organic search does have a pretty high approach. I also have my sales contacts in here. That makes a lot of sense. If we have sales really engaging on those leads, that of course are going to have very high participation. But anytime you could go, okay, we looked at account. Maybe I want to look at this at the global account level, or maybe this is where I want to look at it by opportunity, right? You can always choose to, maybe you want to compare attribution models. So we looked at lead generated by participation. Maybe I want to do marketing channel. And this time, you know what? Let’s actually do last touch. Let’s understand what the last touch marketing effort was for the account. And let’s do it again in the 13 months, right? I want to understand that actual last touch and let’s compare them. So we could start making decisions about participation versus last touch, right? And so we do see, okay, we really are seeing some increased sales contact meetings, which does make sense. Our sales team have those high drives to put in leads. And we also see email, right? So we’re really editing that towards that last touch. Again, this is a nice value and being able to compare these different attribution methodologies. And what I like showing about this is you can do this for any metric, right? So I’m going to do my revenue closed one. We can bring that over and compare. We can also do things like event registration. Maybe we’re taking a look at marketing efforts to our event registration and I want to drag that over. We can always go, hey, let’s actually update this so it gives credit to multiple channels here. So I’m going to right click on my revenue closed one, and I’m going to modify this attribution model. You know what? Let’s actually look at, maybe this time let’s take a look at participation and let’s look at it for the opportunity, right? How did this help drive the opportunity in the last 13 months, right? So you can always come in here, play, click around. It’s not changing any of the underlying data. We are seeing, okay, great. Our sales contact is getting a lot of partnership here. Email as well as our sales contact emails for helping drive those, the revenue closed one, right? So we can help partner with our sales team to tell a great story, right? Where we’re saying, okay, here’s how marketing generated the leads, but here’s how seeing that participation come to lead generated and revenue closed one, right? And like we said, any type of metric, you can always change the attribution methodology because we care about lead generated, we care about revenue, but we also care about engagement. So as we’re trying to drive and understand key elements here, we can take a look at different metrics. All right. So that was a lot of kind of clicking and dragging and dropping. If you do come back to this, we have a nice little summary here of some of the things we walked through. But again, if you take anything away, just right click on the visual, it will unlock a lot of capabilities for you. The next thing I want to show, so you saw accounts and opportunities being able to be modified at the attribution level because we’re in B2B edition, we can also take a look at containers within a visualization itself. So what I want to do here is show one of my favorites, which is our flow visualization. So I’m going to come over and I’m going to drag this over and let’s just bring it down right here. And what I want to see is we’re going to take a look at lead generated as well, but maybe what I want to understand this time is what content were they looking at or engaging with so I can make sure that we’re targeting them with the right content for generating leads. So if we understand behavior, then we can make some actionable decisions from it. So let’s go ahead and look at content type and we’ll let this build out. So we can see that we’re taking a look right now, it defaulted to our, you can kind of see it right here, our global accounts. And that’s great. I want to see maybe a couple of different content pieces that they looked at. So once this kind of adds, we’ll build this out a little bit more so we can see what they looked at. Okay. White papers, testimonials at the global account level. That’s interesting, but maybe what we want to start seeing is, okay, we saw, we took a look at this in global account. Let’s maybe take a look at this. Let’s see. Let’s how did we want to change this? Maybe we want to take a look at it at the buying group, right? So I’m going to, you saw me click on that little pencil and I’m going to click on show advanced settings. And this is where we can change how we’re looking at the data. And I can’t stress how cool this is because if you’re working within CJA Core, it’s always going to default to the person or maybe different events. With this, we want to understand different elements because it’s nice to know how an account moved through. But if we’re really trying to target buying groups, we might want to be very specific and prescriptive and really personalize what the content that buying groups are looking at. So we’ll build this out and go, okay, show me how buying groups moved through my different content types to get to that lead generated. And that’s all within the visualization itself. And again, the difference here being, okay, Doug, I’m going to pick on you. Maybe you’re at Adobe with me, but you’re not on the buying group, but I am. We might want to see what my activity is because, okay, we do see some more case studies appearing in the buying groups. So if we’re thinking about sending our targeting content to our specific buying groups, especially as we’re going through that account orchestration, let’s make sure we’re getting the buying groups case studies because maybe you Doug don’t care about case studies. You’re just trying to learn. But me and the buying group, I am trying to understand how customers are using it. So let’s make sure we kind of target them with those case studies and also white papers and testimonials to make sure that we’re getting the best of the account. So we’re getting a lot of insights into how the different groups are moving through. And I hope you’re starting to see, okay, this is very cool and being able to unlock how these different groups are moving through different types of journeys. And you saw before, you can see it here now, we can always look at intelligent captions too, where it’s going to give me a nice little summary of this flow and what we’re looking at. And like the nice thing is you can always come in here and experiment. You saw me change very quickly from global accounts to buying groups. That’s the power of customer journey analytics. Everything is really done at report time and at speed and scale for these very challenging questions. Okay. So we’re going to look at one more of my favorite visualizations. This is a relatively new one to customer journey analytics. It’s called our journey canvas. This is also specific to customer analytics. It’s not in Adobe analytics, but I built one because, you know, I was thinking about events, right? We have a lot of event registration in my data. I have a multi-step event process and we, you know, I want to make sure that they actually get all the way through event registration and they actually attend that event. Right. So in here we can show the fallout and we see a nice little flow, but the power of journey canvas is going, we can have multiple nodes and we can have multiple start points. We can have multiple end points. We can have multiple dropout points. That’s the power of journey canvas. So for a quick example, I’m going to pick on my marketing channels again, because I want to understand how email, and I’m just going to drag this over and how events or SMS led to event registration. So we just drag over those nodes and I’m going to just zoom out a little to make this easier on myself. And we will just now connect these nodes to that touch point. And this is where we get different from fallout and flow, because now we’re actually seeing how different touch points are leading to this here. And like we can see anytime you could take a look at different containers. In this case, what I wanted to see were opportunities that were leading to this, but my container is by account. So what’s really cool about this is you will see all the accounts that are falling through. So let me zoom back in so you can see this a little bit easier. You can see how many accounts are falling out, but you can see how many opportunities that represents as well. So we’re really getting into great granular access to data that’s really helping us understand at multiple levels what’s impactful here. And this was a small example, but this can be done for maybe we’re trying to understand support use cases and falling out and when are they calling and why are they calling. And you can have nodes down here for calls, right? Journey canvas is a really great and powerful way to say, okay, show me my multiple touch points and let’s connect it together sequentially.

So that being said, we kind of walked through some of the visualizations. We’ve seen containers and how we can use them in action here. We’re kind of getting a little full on time, so I want to hit on a couple of things for sure. And one of the first ones I want to show you is just some high level account level reporting, right? We are now in customer journey analytics. We’re really unlocking the ability to see how our accounts are doing, not just how people in accounts are doing, but tell me information about the account itself. So in this case, I have my opportunities trended for all of my accounts. I can see the amount of closed deals. We’re doing really great. We’ve got some great closed revenue. We also have kind of my top accounts here, right? So we can see the revenue closed one and how many deals that represents. We’re also maybe going, where are we performing well industry wise? And in this case, what I like to start thinking about is, well, what about our satisfaction? How can we make sure that as we’re driving account behavior, we really understand and layer in customer satisfaction to our accounts, right? So in this case, we have some segments of customers with high satisfaction scores. And this is just coming from my survey data, right? It’s coming from my data science team. And so we can see our different accounts here for high satisfaction, medium satisfaction, and low satisfaction. And then of course, the next thing I want to prove out to my team is satisfaction matters a lot because we believe it starts to help drive deal size, right? The more customers are happy, we have an instinct that it says, yes, deals will be larger. And in this case, what we can see because we have that satisfaction information in here is, yeah, there is a nice correlation between customer satisfaction and deal size, right? In my case, my data is showing me 0.96, right? It’s almost perfectly increasing linearly across satisfaction and deal size, right? So we really want to drive up customer satisfaction. And so with that in mind, maybe we want to start understanding industries that we have lower satisfaction in. So when you’re looking at a table like this, well, it looks pretty high. You could always sort the arrow to go, okay, what was my industry with the lowest satisfaction? And in this case, it’s media and entertainment. So I might want to start understanding what accounts within media and entertainment are really struggling, what are driving those low satisfaction scores? So we’re going to build out a table here. So I’m going to dig into that next actionable question, which is, okay, we saw customer satisfaction. We know those drive for large deal sizes. We see an industry that’s struggling. Let’s actually focus on those accounts. So with this table here, I want you to grab account name. We’ll come out of here, grab account name and drag it over. And what I want to do and what I want to show you here is how we can start layering and filtering data. So I’m bringing over accounts. And now I want to additionally filter that. So I’m going to grab industry and I’m going to click this little right arrow to grab my media and entertainment. And you can always filter by that information. Now we’re starting to go, okay, here’s my accounts. Here are my account names. What I really want to see are those accounts with low satisfaction. So you saw above we had segments already created for our low satisfaction customers. I want to come over here and grab my accounts low satisfaction. And this can be, okay, we now see these accounts. Let’s maybe do a specific targeting to them. Let’s get them in some nurture campaigns. Let’s help drive up that satisfaction. This is another opportunity to save these accounts and maybe send them downstream to RTCDP to be used in activation to AJOB to be or MarketoEngage where we can start specific campaigns to really help unlock some of that customer satisfaction. So again, we’re getting into some really actionable insights to really help us understand and drive up that deal size. And so you’ll see if you aren’t following along, I completed this table for you. I also added the we have an account name, but maybe we have some subsidiaries we want to look at. You can always drill in further on a table. So you can bring over new dimensions to break down those dimensions.

Next thing I want to talk about is just, hey, we were looking at high level accounts. Maybe we want to look at specific accounts and how they’re performing. So in this case, I’ve created a dashboard where I filtered down to just one account. And we can see that this account has five subsidiaries. We’ve got a lot of opportunities. We know about 23 people in this account. We’ve had five buying groups in there, and we do see some sessions and some page views in the last three months. So we’re starting to unlock some information about this specific account. Now I’m going to scroll past this because, well, this is very good information. There’s one to a one CJA case I want to unlock with you. And this again is really powerful within customer characteristics. What we’re looking at now is understanding opportunities, right? So we’ve got kind of our traditional opportunity fallout. We can see our different sales stage. We can see how they’re maybe falling out at different sales stages. But really what I want to start asking myself is, well, how long is it taking them to move through the deal cycle? What can I learn about the different sales stages? And now this is a perfect example of a time where, hey, we ingested all that data. Doug, you asked me the question about data, but now what I’ve realized is I brought in the current sales stage. And because everything in CJA is sequential, I know there’s a timestamp in there, but I didn’t bring in like sales stage started at specific data as a dimension. So something we might want to do is go, I need to calculate time between, but that wasn’t already in my data. With customer journey analytics, we have something called derived fields. And again, this is unique to customer journey analytics. So we can go, oh, I really do need to see that time between. We can come over to our data view and I’m just going to pick on the data view I’m using right now. We can come to components and you can always create a drive field. Now, if you’re following along, you probably can’t get here with me because this is under admin access. But I do just want you to see the derived field I created because the nice thing about this is it didn’t require any new ingestion of data. So let me grab my drive fields here. I’m clicking my edit so you can see the work I’ve done. All I did was say, hey, if a sales stage name exists or equals opportunity creation and it’s exists, give me the timestamp. And if a deal has closed, give me the timestamp. And now I’m saying, hey, my output for what I want here is give me the granularity that it took in weeks, right? So I’m doing some date math between those two dates I just took and tell me how much time existed between those two events. This is so powerful for customer journey analytics or for users in general, right? I didn’t have to read and just data. It’s all there. I just had to get a new element I wanted without having to do a bunch of things. And when we set this up, I just walked you through it. It took me about that long to set it up. So it’s just a great way to go, okay, I need to add an element to my data analysis that wasn’t there. And so when we talk about visualization layers, they’re great and powerful and I love them, but CJA is really that business intelligence layer. It can do things like the right fields to really help us unlock those new questions. So I’m going to wrap up here, but I want to show you this field in action. So I created this little histogram. So just in the interest of time, you can always create this on your own to navigate, but I did build it out and we can see we do have some fast closers and we have some opportunities that are slow closers, right? So again, that is taking that derived field and saying give me the number of weeks on average that our opportunities close. So from there, I created a segment of, I think I cut it off about 18 and I called those my fast closers. And then anything above 18 weeks, I called those my slow closers because what I wanted to see was does that impact deal size? And it absolutely does, right? Our fast closers are closing at a much higher deal size than our slow closers. We can also maybe then start to go, okay, let’s understand behavior of those types of opportunities so we can make sure we’re targeting them with the right content. So our slow closers really do like the implementation demystified built for business leaders type of messaging, but our fast closers were like we need scalability, right? We want to maybe target them with the types of messages we sent them in those campaigns. So we’re learning from our previous behavior to help us drive different elements. Now, we could take a look at this by site section and I’m just going to show you that as our final little exercise here. I’m going to replace this services campaign with site section so you can see maybe the different types of information that they’re engaging with. So our fast closers really like those testimonials. We do see that a little bit more in our slow closers as well. So maybe we want to make sure that if we’re trying to drive up those fast closers, we’re really pushing those testimonials. Looks like we really do have some great case studies. Maybe we want to make sure we’re pushing and targeting them with that type of content.

Okay, we’ve got just a few minutes left.

Yeah, look, everybody, hang on for a second. Everybody in through the nose.

That’s a lot. Yeah, this is amazing. And I’m really glad that everybody can kind of go through this again and then like pause and go, okay, I want to, yeah, let’s think about that too. So that is great. Do you have a question? If you have a final thing you want to talk about before I kind of ask a question here, you can. I don’t want to slow you down. No, no, I think wrap up is great. And like I said, you are more than welcome to keep playing with the project. You’ll notice you’ll just need to save as and save it with your initials or something so you can play around with it. But yeah, you can absolutely come in here and experiment, take a look at our demo data and have fun. We try to leave you a lot of different and good notes and types of nuggets. There’s even more in here for you to see and explore. Please feel free to play around. Yeah, this is amazing. This is so cool that people can do this. So I’m going to take a look at it and play with it and again, do a save as and so that you can come back to it, et cetera, later on, show people, et cetera. We did have, I am going to bring up this one up from, I’m going to go Wu Wei 03. Hi team, is it possible to have access, even just read access to the AEP setup for this demo? And so I’m sure that a lot of the people kind of going through this and I guess kind of referring almost back to what I was saying at the beginning, right? This is all kind of built upon the structure and the foundation of the data that you’re bringing. And so the question would be like, am I really getting in my setup, am I going to have access? Am I going to be able to drill down by X, Y, or Z? And so at the end of the day, I mean, it really kind of comes down to the data that they have available, right? To see how deep they can go on or exactly what kinds of events or what kinds of conversion events that they’re going to be able to tie into this.

Yes. And the thing I would add, if you are going, gosh, where do I start? Where do I get data going? For our B2B customers, customer journey analytics is going to have immediate unlocks because you can’t get to account entities and buy group entities. So even if you’re just bringing in your digital data, you’re already going to have some immediate unlocks. And of course, CJA is meant to grow with those use cases. So you can layer in data sources as you grow, right? So you’re saying, well, okay, our Salesforce data is definitely not ready for ingestion yet, but maybe we want to clean up our CRM so we can ingest that data. You can always grow with it so that you can unlock more and more use cases as you go, right? So I think that’s a great point, Doug, that, hey, would my data look like this? Well, depending on what you want to see, it absolutely could, but it’s probably going to look different because your data is just different.

No, that’s great. Yeah. Great point. Yeah. I appreciate that. And thank you to our producer, Sandra, there for putting a link to the page on Experience League where this recording will be. And so we’ll get the recording of this up there tomorrow sometime. And you’ll be able to go through this again. So if you want to click, you can click through to that now. And of course, the recording won’t be there, but you can bookmark it if you want and that kind of thing.

But yeah, I know we’re running out of time where everybody has been so great, so many people sticking around and everything. And you guys were fantastic. This is a great pile of data for us to then to really bring into our brain and sift it around. And so I really appreciate both of you guys on this. And so I think we’re going to go ahead and we’re going to close it out today. And just again, thank you to Caitlin and Carrie for this great stuff about B2B in the CJA. And thanks to everybody who came in and watched and was there for us. And again, you’ll be able to go to Experience League and see the replay on this and still get into that project and play around in Customer Journey Analytics. So thanks everybody. And we will see you next time. Thank you.