ÃÛ¶¹ÊÓƵ

Transcript

Hello, everybody, and welcome. Thank you so much for joining us for this ÃÛ¶¹ÊÓƵ Analytics webinar, as you can see on your screen. The title for this session is Smarter Marketing Starts Here, Integrating Google and ÃÛ¶¹ÊÓƵ. And we’re pleased to have a couple of presenters with us here today who we’ll introduce shortly. But I’m Jeff Anderson. I am the host and producer of this session. Basically, that just means that I’m here to support our presenters and support you as our audience members. And I will hand things over to Danielle Doolin from ÃÛ¶¹ÊÓƵ, who is our speaker for today. So Danielle, go ahead and take it from here.

Great. Thank you so much, Jeff. Hi, everyone. Thank you so much for joining our webinar today. I will be here just to help out in the chat pod. We have our main presenter, Charles Farina from AdServe. I’m really excited about today’s webinar just to get a better understanding, a real live demonstration of how you can really leverage these three amazing solutions, integrating customer journey analytics with Google Analytics data and the Google Marketing Platform. Charles is the expert here, and I’m really looking forward to what he has to share. Please feel free to post questions in chat. And if we don’t get to it in the chat window, we’ll make sure to address at the end. So great. Let’s go ahead and move forward. Thanks, Charles. Yeah, so thanks so much, Danielle. Really excited to be here. So today, we’re going to be talking about how we can leverage ÃÛ¶¹ÊÓƵ with Google, a passion that I’m sorry, a topic that I’m deeply passionate about. So for those of you who haven’t met me before, my name’s Charles. I work with a consultancy called AdServe. We’re actually one of Google’s largest partners in the Google Marketing Platform. We also specialize within ÃÛ¶¹ÊÓƵ, especially in their data and insights products with a deep and heavy emphasis on ÃÛ¶¹ÊÓƵ Customer Journey Analytics. We also do some other things like Target and some other products as well. And for me, this is a topic, as I mentioned, that I’m very passionate about because almost my entire career, I’ve actually spent on the Google side. I consider myself a little bit of a subject matter expert of Google Analytics. I know that product inside and out. And about three years ago, I actually saw my first demo of ÃÛ¶¹ÊÓƵ’s Customer Journey Analytics. And I’ve pretty much fallen in love with that product. And what I’m excited to share with you about today is I think many of us who have been in the space for a long time have often viewed ÃÛ¶¹ÊÓƵ and Google as almost very competitive products. And what I’m going to show you today is how I don’t necessarily think that’s true anymore. I actually think they’re very complementary in nature. And that’s the theme of what we’re going to be sharing today. And a lot of this is going to be real world actionable insights and learnings because this is how we’re helping a lot of our customers that we work with today that have either grown up on one or the other side of the space. So with that, we’re going to talk through our quick agenda for today. I just have a few slides to kind of set up what CJA is. We had the poll at the beginning. So I think we have 50% of you that are somewhat familiar with CJA, or Customer Journey Analytics. I’ll call it CJA to shorten it. And 50% of you that are brand new to it. So I want to level set for agenda, just do some quick intro of what Customer Journey Analytics is, why we’re talking about it, what you need to really know about it. Then actually for 90% of this webinar, we’re going to do a live demo. And in that live demo, I’m going to show exactly how we can leverage Google to bring and integrate into ÃÛ¶¹ÊÓƵ, where they both can play together. We can also talk on some of the strengths of both ecosystems. And then we’ll go through some live analysis kind of in to really showcase the potential and power. And then as Danielle mentioned at the end, we’ll save some time for some Q&A and make sure we get through your questions as well. So I fell in love with analytics, I would say about 11, 12 years ago coming out of college. I had some internships. And in my internships, I had a background in marketing. I got to try all these different areas within the business. I got to do email, SEO. I also got to try web analytics. And I found myself really gravitating towards the web analytics side. I love building puzzles. I felt like analytics was a little bit of putting pieces together to put together that kind of full puzzle. And for me, kind of coming out of college, I loved ÃÛ¶¹ÊÓƵ Analytics and Google Analytics and I felt that those products really with just a little bit of training, you could get really deep into answering really important questions about the business. And that self-serve analytics was for me where my passion kind of really lied and I’ve really followed through today.

However, kind of fast forwarding for the kind of slide you see today, analytics is extremely different than it was, or more years ago. I feel that many organizations that kind of mature in their analytics capabilities, oftentimes in many ways can actually mature or graduate out of, let’s say a Google Analytics or an ÃÛ¶¹ÊÓƵ Analytics. Many of the needs, the use cases, the reports you want to create aren’t necessarily available in the platforms that you might be using today. Some of the customers, for example, that I work with, at some point in time start to heavily leverage the data warehouse integrations. So for example, with Google, you’d use BigQuery and then you’d put Tableau or Looker on top. If you’re ÃÛ¶¹ÊÓƵ, you’re using your data feeds out of ÃÛ¶¹ÊÓƵ Analytics and same thing, you’re putting Tableau or Looker on top. And often for some customers, you end up actually doing almost all of your reporting and your analysis outside. And you’re just using like a Google Analytics or ÃÛ¶¹ÊÓƵ Analytics as like a clickstream data feed into your data warehouse. And that’s fine. And I actually think that’s what all of you or every customer should aspire to do. But the challenge along that journey is that that self-serve analytics capability kind of disappears. In this data warehouse model, you need someone to write SQL, do data engineering, you need a Tableau expert, and being able to drag and drop, interact with the data, quickly find insights, becomes more challenging than ever before. So this is where ÃÛ¶¹ÊÓƵ kind of enters the conversation. I’m really excited because ÃÛ¶¹ÊÓƵ has a next gen solution that’s completely different than either Google Analytics or ÃÛ¶¹ÊÓƵ Analytics. And while it has some feature overlap, especially for kind of classic web analytics, it actually unlocks completely new analytical capabilities. And it meets the challenges that a lot of marketers and businesses have today. And for me, it’s simply bringing back self-serve analytics. And we’ll go through that through our demo today. So as we’re kind of exploring, this is traditionally what a stack might look like for any customer as far as analytics. We’ve got event streams or event sources like your ÃÛ¶¹ÊÓƵ Analytics or Google. You’ve got your data warehouse. You’ve got your BI platform. You also have some transformation kind of happening in the background, either a product or an actual employee kind of doing all that. And again, it’s causing that self-serve analysis to be kind of more challenging. So some real world examples of why this is a challenge. Let’s say you’re a retail customer and you have online orders and you have in-store orders. Connecting in-store to online is typically something that would never happen in a Google Analytics or an ÃÛ¶¹ÊÓƵ Analytics. Let’s say you’re a B2B customer and you have long sales cycles, or you simply have a CRM and you have a Salesforce or Salesforce and you have customer interactions that happen on the phone or in email or after the lead form is filled out. Traditionally, something like a Google Analytics or an ÃÛ¶¹ÊÓƵ Analytics does a really great job of measuring the upper funnel activity. But once they’re in our CRM, following that lead downstream is typically an exercise of forcing that data back in, doesn’t really work. Or let’s just take a basic use case that everyone has today. All of us have websites and I find in today’s world, even doing some basic analysis like understanding what content leads to outcome is more challenging than ever before. And this is where customer journey analytics, what we’re gonna go through today can really help. And what I love most about it is simply, it is a data visualization layer or platform that can sit on top of all of your existing infrastructure and products. So what we’re gonna go through today doesn’t replace anything that you’ve built or maybe building today. So if your organization’s building a marketing data warehouse, if you’re already invested in Tableau or Looker or you’re already using Google products, what’s awesome about what we’re reviewing today is this is additive. It doesn’t have to replace anything you’re necessarily doing. In fact, some of the best implementations or use cases I have for customer journey analytics within my customers are where we actually stand it on top of a data warehouse. So it can integrate with Google Cloud, it can integrate with AWS, it can integrate with Snowflake. So in my intro, I talked about how analytics today is in many ways one direction, where we take our clickstream and we put it in our data warehouse and then our journey usually ends in a data visualization platform. CJA and ÃÛ¶¹ÊÓƵ is bi-directional. So it can both send data to your data warehouse if you want the clickstream data, it might be collecting, but it can also send data and receive data as well. So I can receive in-store transactions, I can receive CRM data. And this is all possible because we’re gonna talk through how this platform does stitching, it accepts historical data, and it can do all sorts of things again that are classic solutions we’ve all grown up with like Google Analytics and ÃÛ¶¹ÊÓƵ have really never done before. So with that, some of the key things and I’ll just save these slides more for reference is that CJA is really different than anything else that’s out there. And for me, it’s different because this platform, it’s gonna unlock some key capabilities. So when we’re talking about integrating Google and ÃÛ¶¹ÊÓƵ together, some of the challenges that this platform can solve for customers that are heavily invested in Google is that it can unlock kind of necessary functionality that you might not have today in your existing Google tech stack. So as an example, this platform actually can stitch data together retroactively. So Google Analytics as an example, it doesn’t take an anonymous user that becomes known and restate the known behavior back to their anonymous behavior before they opted in and told you who they are. This platform can take that exact data feed and take the IDs that your users have and associate it backwards in time and actually put the customer journey together. Additionally, other kind of key benefits is that it will unlock on the fly data transformations. So as an example, building something simple like a marketing channel report. Let’s say we have messy channels, messy UTMs. This platform can actually let you recreate and reclassify any dimension. So we could lowercase, uppercase, we could recase, if you look, or if then statements to actually clean and massage our data directly in platform, again, without having to leave into BigQuery or Looker and kind of clean and make our data presentable. So with that, I’m gonna jump straight into a live demo and really talk about how this can all work today. So give me one second while I share my screen.

So for this demo, I really wanna showcase some of the main capabilities of how everything can kind of integrate and play really nicely between Google and Google. So on my screen, what we’re looking at is we’re looking at customer journey analytics. So for about half of you that filled out the poll at the beginning, if you’ve never heard of customer journey analytics before, this is the next generation or the re, I would say evolution of ÃÛ¶¹ÊÓƵ Analytics. So it keeps the front end of ÃÛ¶¹ÊÓƵ Analytics. If anyone has ever used ÃÛ¶¹ÊÓƵ Analytics, if you’re using it today, I’ve ever seen it. It has the same workspace where we create reports. This is a very feature rich platform. If you’re coming from Google or using Google today, it’s almost like a Looker Studio and Google Analytics came together as a single product. That’s what workspace and customer journey analytics is The biggest benefit for anyone that’s ever used ÃÛ¶¹ÊÓƵ Analytics is ÃÛ¶¹ÊÓƵ Analytics has often been viewed as a very enterprise or very powerful, but often maybe sometimes it’s hard or not necessarily as intuitive to use as something like a Google Analytics. ÃÛ¶¹ÊÓƵ’s completely rebuilt the back end of the product and made huge updates on the front end. So we don’t have Ebars, we don’t have props, we don’t have the most complicated parts that were confusing to some ÃÛ¶¹ÊÓƵ Analytics users or especially users coming from Google. We’re gonna go through our demo. There’s lots of updates that ÃÛ¶¹ÊÓƵ’s made to modernize this platform and bring it into the benefits that we’re gonna go through today.

So first of all, one thing to point out in this demo is I have a demo of Google Analytics data inside of customer journey analytics. So the report I’m looking at here is actually live Google Analytics data. So how is this possible? Well, this is some of the ways that we can start talking about how Google and ÃÛ¶¹ÊÓƵ can work better together. So if we just search for example, GA4, CJA historical data, ÃÛ¶¹ÊÓƵ’s got a full guide on how you can ingest Google data into customer journey analytics. This is possible because when ÃÛ¶¹ÊÓƵ rebuilt the back end of the product, one of the things they did is they completely opened it up. So historically with Google or ÃÛ¶¹ÊÓƵ, you had to use client side tags, where we’d add pixels on the website to collect user data. We can do that with ÃÛ¶¹ÊÓƵ, but what we can also do is we can also collect data from where it already exists. So as an example, anyone that uses Google Analytics has access to a Google Cloud or BigQuery integration where you can ingest all that historic, or sorry, all your clickstream data in a table in Google Cloud. And in ÃÛ¶¹ÊÓƵ, there’s a source connector where we can pick that data up. So this also exists just to call it out for any data warehouse. AWS, Snowflake, Azure, we can even have direct connectors to Salesforce. This product is called customer journey analytics because ÃÛ¶¹ÊÓƵ’s opened up the possibility of ingesting customer data from wherever it lives. It doesn’t just have to be collected in real time on the site. And I’m gonna talk about this over and over again, but as part of these capabilities, it also accepts historical data. If we had a year of Google Analytics data sitting in BigQuery, because we’ve had that integration running for some time, we can bring all that data in. And in addition, there’s that stitching capability. So remember, in this platform, I can also take IDs and associate it with other data sets or data sources, and also associate that retroactively, things that Google has never really done. And the reason this might be really important is as we’re talking through use cases, I’m gonna show two, I think, key themes in here. One is how CJA can be a massive upgrade for unlocking self-serve analytics. And this can be for just basic analytics. And when I’m talking basic, it could be literally just trying to associate what content is leading users to actually convert. What pages or what interactions are leading those users to convert. And then we’re also gonna talk through some of those offline capabilities as well, or the art of the possible. What if I’m that B2B customer, and I wanna put together the ultimate B2B funnel? Or if I’m a retail customer, and I really want to know for anything that’s happening in my physical locations, did online contribute at all in that particular journey? Did our email channel contribute? Did a physical onsite interaction help? Did both? How could I do some of that exploration? So let’s start our journey together. So in this report, we can load this Google data, again, from this GA4 BigQuery feed. ÃÛ¶¹ÊÓƵ also launched an integration with Google Tag Manager. If anyone is heavily using Google Tag Manager, you can actually just leverage the Google Tag Manager data layer, and ÃÛ¶¹ÊÓƵ can sit on top of that, and you can basically quick start feeding live data into the ÃÛ¶¹ÊÓƵ Experience platform. And why would we wanna do that? Well, some of the benefits would be that immediately, it unlocks this functionality that you’ve never had before. So I can stitch user IDs, I can connect it to other data sets, but even some basic things like, imagine if there was no cardinality. Imagine if there was no more sampling. So if you’re a customer with a larger set of data, doing something like this is not really possible in Google Analytics today. Like we’re not supposed to collect user IDs as a custom dimension to look at individual user behavior. This kind of analysis is more appropriate in BigQuery because again, there’s a sampling or cardinality constraints that don’t let us get that granular with the data.

All of that is eliminated immediately within this product. And if we wanted to just talk about basic reporting, again, one of the benefits is like, imagine if Looker and Google Analytics came together, so you could more quickly do your analysis without having to bounce between both platforms. So by simply having this stood up on your Google Analytics data, let’s just create the world’s most simple table. So ultimately what I wanna do is I wanna do some content analysis. I wanna understand what content is leading to outcomes that I care about. And to start our adventure for content, let’s just create a simple content report. So in this content report, I’m just gonna drag over my page title and it’s just telling us our top content that’s been viewed on the website. For many of us, immediately when we start doing analysis, we encounter challenges or the need to manipulate the data. So as an example, right now this is using the Google Merchandise Store and in the Google Merchandise Store, you can see that our page titles aren’t really easy to use for reporting or analysis because our SEO team got a little creative and started to stuff keywords all over our page titles. So if I wanted to make this kind of more easy to use, maybe I would want to actually remove the brand name from each page title so it’s a little cleaner. Or I might wanna start grouping my content because eventually I’m not only gonna wanna understand what content is leading to the outcomes, but what kind of groupings of content are leading to those interactions. And I also might wanna understand how content kind of flows within a journey. So did they start, for example, on the homepage and then go to a product page? Did they start on product pages and then maybe see the homepage along with a journey? Do they interact with multiple categories of my content? And again, in GA, this can be a challenging exercise for a few reasons. One, it’s not easy to manipulate something as simple as a page title in your reports. Second, it’s gonna be difficult to associate the outcome at a page level. So let’s kind of tie that together. So first, one of the awesome things about Customer Journey Analytics is, again, it’s modernized a lot of the front end and the back end of the product. So what I’m gonna do is I’m gonna actually open up the data view for this report. And in the data view, one of the key benefits is that the complicated parts of ÃÛ¶¹ÊÓƵ Analytics are gone. So when we set this up, the way CJA works is it uses a modern schema approach. So in Google Analytics, a lot of people have heard of an event-driven data model. Customer Journey Analytics has a same kind of event-driven data model. Everything’s driven off of events. But one of the major differences is that Google Analytics has a schema. So for example, if you’re an e-commerce customer, you have to tag products and SKUs and arrays and the checkout steps in very particular precise ways in order for it to show up in Google Analytics correctly. In ÃÛ¶¹ÊÓƵ, there’s kind of recommended schemas or ways you’re supposed to tag things. But at the end of the day, you can create whatever schema you want. So as an example, right here, I actually have a metric for calls. Later on in this demo, I’m gonna show you a call center dashboard. A call center dashboard is for our call center where people call in. It has nothing to do with the website by default. We can create a schema for call center reports. So the point here is there’s no more EVAs, there’s no more props. We still can set attribution and persistence, but we do it in a much more modern way where you can craft your own data models and customize it to exactly match the taxonomy that your business uses. So for the reason this works is in ÃÛ¶¹ÊÓƵ, I can take a Google Analytics schema, so that GA4 BigQuery schema, and I can simply map everything together with the schema path to that BigQuery table and then name it whatever I want so it shows up exactly how I want it to when I’m doing my analysis in the product. So this is how we bring that Google data into ÃÛ¶¹ÊÓƵ on the fly. Now, what we were doing is we were interacting with our page title. And one of the things I mentioned is that I might want to do something like this. Wouldn’t it be nice if I could take my page title and maybe just rip out the Google Merchandise Store from it and just create a new version of my page title that’s clean without my brand name repeated over and over again. ÃÛ¶¹ÊÓƵ has a feature called Derived Fields. And for me, this is amazing. So imagine if you had filters, and filters allow you to change your data, right? We could lowercase, we could find and replace, we could do regex. Google Analytics in Universal had filters, and filters were only point in time forward. So if you wanted to lowercase your data in Google Analytics, you could only do that point in times forward in the old version. In GA4, we have a very limited set of filters. And right now, like you can’t lowercase, you’d have to actually do that in Google Tag Manager. ÃÛ¶¹ÊÓƵ’s doing something very different. So they have filters, we call them kind of derived fields. And derived fields are actually retroactive. And this means that we can take any dimension that we’re collecting, your UTMs, your page title, your products, any dimension, and you can change it on the fly, apply any operator to it, and it’s like it had always existed that way from the first place. So some other quick examples are, we could take all of our content and do on the fly classifications of our pages. So imagine if we had content groups, right? I could take any page that contains apparel or home, and replace it with emojis. I could classify my marketing channels into whatever groups I want, and off of any dimension, not just my UTM parameters, but I could also leverage onsite activities into the channel groups itself. And the way this works, if I go back to my report, I can just drag over my new page title that we created seconds ago, and we’ve now classified our content on the fly with these derived fields. So something as simple as building a basic content report is something that CJA, like with Google data, can allow you to do, and actually do the additional permutations, or questions, or classifications you might want or while you’re doing your analysis without ever leaving the platform. So this is just a basic table. We’ve done nothing revolutionary here. But what we’re after with our exercise is we wanna understand what content is leading to an outcome we care about. In Google Analytics, this is not possible in the platform today, because attribution largely exists for channels, campaigns, sources, mediums for your campaign fields. There’s never been an attribution at a content level, or really an attribution at an interaction level. That’s again where you’d have to go into BigQuery and create some of that analysis on the fly. So let’s showcase how ÃÛ¶¹ÊÓƵ could help with this kind of Google integration that we’re running today. So as an example, let’s say we’re a retailer and we’re measuring orders. So in ÃÛ¶¹ÊÓƵ, I could drag over my orders metric onto this table. And in this orders metric, it’s gonna do what Google Analytics does by default. So we can have it do the last page. So this orders metric is showing us the last page that the user purchased from, which is the checkout confirmation page. This doesn’t help me because I wanna know what content is contributing to an order, not what the last page that an order resulted from.

The beauty is, remember those derived fields I just showed you? Imagine if you could create a metric and in that metric, you could assign whatever attribution you wanted to it, and you could change it at any point in time. You could even make it the default for everyone or just for you. This is how ÃÛ¶¹ÊÓƵ works. So I have this other orders metric, and this is based off of a derived field where I created the orders, but I applied a participation attribution model to it where all it does is it simply takes every page that preceded an order and just shows you what content shows up most frequently or least frequently within that journey. There’s another way to visualize this. So ÃÛ¶¹ÊÓƵ’s got this attribution panel and as I was mentioning, ÃÛ¶¹ÊÓƵ does attribution very different than other platforms. It allows you to do attribution of anything. Attribution is still important for every business and it’s not just for marketing channels. Again, let’s say we’re a customer like GoPro, we want to do attribution of our content. We’ve been talking about that. I might also wanna do attribution of my site search. I wanna figure out what users are searching for, what keywords are not resulting in anything being found or them actually interacting with a search results. I might wanna mine this for new content that I’m creating or curating for my customers. So doing attribution at a keyword level is important and ÃÛ¶¹ÊÓƵ allows you to do this. We can do attribution of anything. So I could pick my orders metric. I can pick my UTM parameters or my site search, or in this example, my page title. I can pick my attribution models and create a report like this on the fly. I wanna know what content contributed, but I also wanna know what content acquired the user. So in Google Analytics, we have a landing page report and the landing page tells you what was the first page of the session, but I wanna know what was the first page the user ever saw that eventually led them down a journey where they ordered. And in a matter of seconds, I can create this report that I’ve never had before on my Google data that answers that exact question. So for every order, right now we’re doing a 30 day look back window. I could change that. I could do 90 days. I could do it at a user level. I can also do it at a session level, all sorts of customization. But right now, just for some basics, we’re just doing the defaults. And it’s telling me for every order, 53% of them started with the user seeing the homepage as the first page they ever saw on our website.

And then on the right, we have that participation model where we’re seeing what content shows up most or least within that journey. And remember those derived fields we created, like maybe we’re doing some content classifications. I can actually drag those over into this attribution panel or even drag over my channels to do additional analysis with this on the fly. So super flexible and super customizable to do this. So that’s just some basic analysis and how that works in ÃÛ¶¹ÊÓƵ. We can do this all directly in the platform without ever leaving it. Now, let me talk through another challenge or example that I find many customers struggle with. So let’s say we stay in this retail example. So let’s say we’re GoPro and we wanna measure our checkout flow. This can be a very challenging exercise in Google Analytics and ÃÛ¶¹ÊÓƵ Analytics or really in any platform that I find today. And it’s a challenging exercise because often when we talk about a checkout flow, we think of funnels and funnels are great, but they can be really challenging in today’s kind of marketing world. ÃÛ¶¹ÊÓƵ’s got funnels in CJA, but funnels are really just where we have a predefined set of steps. And the challenge with the experiences customers have today is often the set of steps or the experience itself changes based off of how and in what context they’re interacting with us. So if they’re on desktop versus mobile, they could have a completely different experience. Same with mobile app. If they’re also logged in or authenticated, they might have less steps in the checkout funnel because we already pre-fill all that information for them. Or even what product, if they’re buying a subscription versus an actual product, that can also completely change. Same with upsells, right? So something as simple as a purchase or a checkout funnel can be very challenging with a classic or traditional fallout. And it’s same with flowcharts, right? Sometimes we’ll think of flowcharts as maybe being able to help, but equally kind of challenging. So here’s one of the things that I’m really excited to show with you is that in CJA, ÃÛ¶¹ÊÓƵ’s built something called Journey Canvas. And what Journey Canvas is, is it’s like if funnels and flowcharts came together and created a new modern version that allows us to do two really important things. This is a fantastic tool to do ad hoc on the fly analysis. So imagine if we were GoPro and we wanted to know, for example, how many users go through all the permutations of our checkout flow. I want mobile, I want mobile app, I want desktop, and I just want to be able to understand how many users are in each of those funnels. So just doing some ad hoc analysis on the fly, this is going to be fantastic to allow us to do. Additionally, we might want to actually create a set funnel or a set path and then monitor that over time. So if I’ve defined my ultimate like multi-pronged checkout funnel, we could actually save that and then come back to it and see it and layer in how different interactions kind of improve or change that experience. So here’s how this works. So in this Journey Canvas, what this is going to allow us to do is it’s going to allow us to build that kind of ultimate checkout funnel. But first we’re just going to start with some of the ad hoc analysis. So one of the cool things about Customer Journey Analytics is if you’re coming from Google, Google’s always had accounts, profiles, and in GA4, we’ve got properties. If you’re an enterprise customer, you might have sub properties. And that’s where we’re able to switch between different data sets that we’re kind of collecting. ÃÛ¶¹ÊÓƵ’s got a similar concept called data views, but one of the awesome parts of it is that when you build your ÃÛ¶¹ÊÓƵ view of all of your data, you can actually customize this on the fly. So I can have a data view of all my data together. And in this case, I’ve got GA app and call center, and then I can create or connect to any other data on the fly. So maybe I only have in-store, maybe I only have online, maybe I have a different brand. ÃÛ¶¹ÊÓƵ actually lets you, if you have access to all the data, switch between any of your data sets, even in the same workspace that we’re in. So in this example, I’m gonna switch to a different set of data where I’ve got some retail data. And in this retail data, I’m gonna drag over the date range I care about. So we’ll grab in this month. And what we’re gonna try and do is build a checkout kind of flow on the fly. And first, I might just wanna do some of this analysis. So as an example, in this demo, I’ve got mobile app, web, and desktop, and we’ve tagged the website to track the different steps in our funnel. The way Journey Canvas works is we can just drag anything we want over to the canvas. So I can drag like step one, I can also shift click and drag all my steps and bring those over. And what it’s doing is it’s just counting the number of people that interacted with each of these. So I can try and put these all in order. So I’ve got my app, I’ve got my purchase confirmations, I’ve got my mobile web. I think down here, I’ve got mobile web purchase too. I can rename these. So I could say this is desktop, for example, add to cart, because I know that’s what step one really is. This might be the checkout page for desktop. And it allows me again to curate this however I want. So right now it’s just showing us the amount of people that have interacted with each of these. If I go back in this canvas, I can also drag anything. I have all my metrics, all my dimensions, and even my segments. So I could drag over all my users, and I might wanna know, okay, for anyone that visited my site, we’ve got 97,000 users. How many of those, for example, got to our start checkout on desktop? And if I associate this arrow, it creates this connection. So now I can see 4% of my total users did that on desktop. I could also drag this to app and also mobile web. So here it’s letting me actually flow through those experiences.

And if I just wanna complete this journey, I could then associate each of the checkouts with each other. So I’ve got all my app and web checkouts, and then I might wanna bring them together.

But wouldn’t it be nice, instead of actually having three separate order confirmation pages, if I could just have one. So I’m gonna delete these, and we have a metric for orders. And if I just drag my metric for orders over, I can reunify all my checkout flows together to my master kind of order metric.

And here we go. So I’ve got that checkout flow across all my experiences created. I think I did this in under a minute. And there’s a lot of additional customization we could do to this. At the top, I could start changing the percents. So I could do the percent of previous node to kind of make it function more like a funnel. I could also check this box to drive fallouts directly in the report to see how many abandon. I can also right click on these and apply additional information to it. So maybe for that last order, I would love to know if it could actually show me the marketing channel, the order resulted in directly in my journey canvas. Just note that also at any point in time, you can actually create reports, you could create audiences, you can do all sorts of things to do that additional analysis. I can do this all on the fly. I could even take this further. So a lot of customers will create order flows, but ultimately one of their metrics is always an additional order. We want the customer to actually have a second purchase. We want to increase their LTV. And that additional purchase can be very challenging to layer in in a lot of platforms like Google Analytics. In ÃÛ¶¹ÊÓƵ, if I just drag my orders metric, I can just add that here. And on the fly, it’ll tell me that exactly 10% of my orders are reordered. So we had 1900 people, 196 reordered, or that 10% actually went through and did a second order in this date range I’m looking at. So this is for me amazing, because again, if we just think of the possibilities of doing ad hoc exploration, or trying to do analysis across all the experiences that we’re curating and offering our users, I’ve never really had access to something like this that allows me to actually come in and actually build something to do that directly in platform.

We can actually take this way further. So, so far in this demo, we’ve largely only kind of had this point of view or lens of classic web analytics. I’m just kind of tracking what’s happening on our website. In the beginning of our demo, I showcased how in ÃÛ¶¹ÊÓƵ we can do a lot more. So remember that one of the things ÃÛ¶¹ÊÓƵ did is they completely opened up the backend of the platform. So if we’re an organization who has invested in building a marketing or a data warehouse for our enterprise or our business, we can bring that in. So we have all these cloud connectors like Google Cloud. So the reason this is important is, again, I might have some B2B customers who have tried to connect online with CRM. And the only way to really stitch that data together is to do that in Google Cloud, where you bring in your Salesforce data, you bring in your Google data, where we have an ID in common, like in a form submit, we might actually grab the Google cookie, I grab my CRM ID, I put them in a common event, so I have a join. And then I try and stitch that to the downstream activity. All that analysis is gonna be through Google Cloud and Looker. Now, one of the beauties of what we’re talking through here is this doesn’t replace any of that work. So if you’ve already warehoused all of your data in Google Cloud, we can actually connect to it with ÃÛ¶¹ÊÓƵ. And this can be the visualization layer to bring that journey together. In that retail example, this is just measuring orders on the website. If I’m GoPro, as an example, I might have actual physical stores or physical kiosks, for example, at the airport. And I might wanna layer in how those customer touch points happen in those locations. And we can do that with customer journey analytics. So let me switch to a different demo set I have. I have a different demo set of retail data, and I can show you this in a few different ways. For this additional demo set, I’m gonna pop open a workspace report so you can see the art of the possible. And the art of the possible is largely gonna be what we can actually do if we brought in all of this additional data. So in this workspace report, we’re bringing in ÃÛ¶¹ÊÓƵ Analytics, we’re bringing in in-store, we’re bringing in Call Center, and I’m even bringing in some of my messaging. So I’m actually bringing in SMS sends and SMS opens. So the send, not just the open or the click, but the full SMS journey is kind of coming in here. And this allows me to create reports and visualizations that, for example, would never be possible in something like Google Analytics. It isn’t meant to do it. So we talked about Call Center data, for example, very briefly. You could use a platform like this to build a Call Center dashboard. So how many calls are we getting? What is the duration of the call? What are the reason people are calling? What Call Center are they actually coming from? All of this, we could build a Call Center report, has nothing to do with the website. But what Customer Journey Analytics does is it allows us to connect different datasets together where we have IDs in common. So as an example, if I have an ID of someone authenticated on the website or someone that came from an email campaign, I know who they are. And if they call our Call Center and I authenticate that user or the agent asked them to identify themselves with their customer ID, I can create a report like this, which simply says, show me what was the last page someone saw on our website that preceded a call hitting our Call Center. And why would that be important? Well, if we have a Call Center, one of our business metrics that we’re KPIs against might be to deflect calls on the website. We don’t want users to necessarily always call us, especially if there’s opportunities, for example, to improve the content on our website to deflect that call. So think support as an example. If we wanna figure out what support articles aren’t answering our customer questions that layer into a call and then the reason they’re calling, I have an out of the box report in CJA to do this. Additionally, if we go back to that CJA report, I just wanted to layer in to outro. In that journey canvas, we could do really cool stuff. Like if I’m a retailer and I’m bringing in, for example, those in-store orders, what we could do is we could pop open a journey canvas. I could, for example, connect this to that retail data that I have, drag over the date range I care about. And in this sandbox I have, when I look for orders, I have different types of orders. I have online orders, I have mobile app orders, I have in-store orders, I even have call orders, and I might even have my total orders metric as well. And in here, we can actually, if we wanted to, connect those different experiences together. So I might just simply wanna know how many people, for example, who did an order, ever, for example, called us later. And I could just connect this together with that arrow and it’ll associate and say, okay, of our 2,600 people who ordered, 2,000 eventually placed a call order as well. Or I might wanna know how many people who, for example, placed that online order ever showed up in our store later. I could just connect that journey together to do that analysis. I could right click, for example, on this node, and then bring in, for example, a breakdown of the actual product they purchased or anything else that I might wanna know directly in this journey campus. So this platform is built to, again, sit on top of a data warehouse, where if we’re orchestrating all of our customer data and collecting it in a universal replace, this can unlock the deep analytical capability for you to drag and drop, do attribution, do pathing, really understand what the customer journey is. For me, this doesn’t replace anything you’re doing today. It doesn’t replace your data warehouse. It doesn’t replace something like a Google Analytics. It doesn’t replace your data visualization platform of choice like Power BI or Tableau or Looker. But this creates a much more modern front end doing analysis on the fly, that self-serve analytics we really talked about at the beginning. So this really just starts to touch on the possibilities of customer journey analytics. Our goal today was to talk about how Google and ÃÛ¶¹ÊÓƵ could potentially play together and what benefits it might unlock for your organization. What we highlighted through our demo today was how we could load easily data from Google into the platform, and why we might wanna do that. And that’s to unlock a plethora of functionality on top, where again, no sampling, no cardinality, bringing in offline data, stitching activity together, both backwards and forwards, all immediately available for you. I ended with Journey Canvas, which I think is a great way to showcase some true next gen functionality in here. And again, all of this just really scratches the surface on that. So with that, I think this is a great place to end and see if we have any additional kind of questions.

And thank you so much. That was awesome. I really loved the demo. And just, I think towards the beginning of the session, there were some questions around like, well, why wouldn’t you use Looker? Or why would you bring GA4 data to CJA? Do you wanna kind of, I mean, you showed some great examples with direct fields and attribution, and then kind of elaborate on, just gonna reiterate what you went through, why you can do more kind of flexible data configurations in CJA. Yeah, so I can answer a few of these questions on the fly. So if you’re getting, so we had this one question on Looker Studio, like can do a lot potentially of the visualization layer of what we showcase today. And that’s true, but often what you’re going to find is you have to have the perfect data models behind it to empower any of those visualizations. And in fact, anytime you have additional questions in those visualizations in Looker, often it might actually require you to go back to the data itself and create different subsets or different aggregations. And or you always are going to maybe have to worry about the compute cost behind it. So when we’re like drilling into individual user behavior and very large data tables, layering in content and channels together, those permutations can oftentimes result in some very expensive queries coming out of your data warehouse. So many like very advanced organizations are uncomfortable with the level of granularity we’re going through today coming out of that kind of data model without a lot of guard rails attached from it. So the beauty is again, like Looker should absolutely be doing what we’re doing today, but this platform is kind of on the side or parallel to it where an analyst can do things in a very safe curated way where it’s not gonna run into a lot of those kind of concerns or considerations. So we’re finding a lot of the power users that love customer journey analytics are actually coming from the BI side of a lot of organizations. They love the idea of the marketers having access to the same data they’re already curating for their users in a much more kind of organizer again, kind of curated experience. So that’s a really great question.

There’s some other questions in here about resources. We’ll share all the resources. We dropped a few in chat about how you can ingest all of this data.

There’s some questions on, I think at the end of guard rails for CJA. This platform can work with very small data sets. It can also work with very massive data sets. So think of some of the world’s biggest organizations. ÃÛ¶¹ÊÓƵ’s already got them on this platform. So it can handle billions of rows and data, no problem whatsoever.

I think Rahul asked if we could do journey canvas on a user or a visit level. Absolutely, if I were to share my screen again, when you actually click journey canvas, the first thing you do is you specify if you want it at the visitor level and the settings, that’s also possible. By default, it’s all at a user level, which I think is actually what it should be because often we wanna actually journey in canvas at that user level.

Did you do there, it was a question on users. How do we actually track users? So this is a good question. So in Google Analytics, one of those challenges I mentioned was it doesn’t stitch users backwards. So if we had a user that was anonymous a week ago, and now they’re known and we had a cookie to create it, even though Google can associate that cookie, if it wanted to, it doesn’t. So in Google Analytics, it actually tracks two users. You’ve got the anonymous and you have the logged in. So it’s kind of two users that sometimes can be challenging because when you’re looking at user counts, it’s not always de-duplicated in the way you might think. ÃÛ¶¹ÊÓƵ is simply gonna de-duplicate that. So once we stitch that ID backwards, it only shows one user and we have all sorts of controls. We can have a whole hour session on just stitching. ÃÛ¶¹ÊÓƵ has a ton of stitching functionality in there for you to have ÃÛ¶¹ÊÓƵ do stitching for you, but it also can accept your stitch data. If you’ve already solved stitching on your end, ÃÛ¶¹ÊÓƵ can actually accept your stitched IDs and we can use that to kind of power everything in the platform.

There was a variety of questions in here around ad connectors. So a few things about advertising integrations. ÃÛ¶¹ÊÓƵ has a ton to offer in this space. There’s other ÃÛ¶¹ÊÓƵ platforms that we didn’t talk through today. ÃÛ¶¹ÊÓƵ has a real-time CDP, where if we wanna activate data in real time to Google media, Google ads, DV 360, we could use real-time CDP to do that. ÃÛ¶¹ÊÓƵ also has ÃÛ¶¹ÊÓƵ Mixed Modeler, which does incremental or kind of that MMM flavor of marketing. So if we wanted to not just do attribution, but also do incremental measurements, ÃÛ¶¹ÊÓƵ actually has a mixed modeler solution, which can again showcase how Google media is actually flowing through to the experiences that you’re driving. In customer journey analytics, ÃÛ¶¹ÊÓƵ also released something called, I think it’s a aggregate data or, sorry, summary data in the platform. What summary data allows you to do is NGA, we have that Google ads report where we load aggregate impressions and clicks and ROI. Through summary data, you can actually bring in those same Google integrations if you’d like. So think Google search console, think Google ads, we could do that. One of the things though is I don’t find every ÃÛ¶¹ÊÓƵ customer does that because as I said in the beginning, we don’t have to think of this whole conversation as competitive to Google. This is all very complimentary. For many of you, I think it’s important to call out, there’s always a free version of Google analytics. You can send 20 billion events per month to free GA. And with that, you could always have GA running in the background where GA has all those reports and CJA is more connecting to the omni-channel view of the customer. And Google always has other things. Google has consent mode, Google has Google signals. So for some customers that are heavily using Google for media buying, this better together story can be really important, especially if we’re trying to maximize Google media and ÃÛ¶¹ÊÓƵ together. So there’s different pieces to layer in. Daniel, anything you wanna share in closing? No, thank you for the great webinar with some really excellent examples. Thanks so much, Charles. All right, thanks so much for everyone for joining.