All you need to know about Cross Device Analytics and Customer Journey Analytics

Transcript

Please welcome to your screens, Sonya Charles. Hello and good afternoon everyone. Thank you for joining me this afternoon on today’s session, which will be analyzing user behavior with cross device analytics and customer journey analytics. In today’s session, we’ll be focusing on these two Adobe applications with a real emphasis on explaining how they can help customers and brands to really first of all develop a first party data strategy. So really develop the understanding of how with the sort of fall of third party cookies, which everyone seems to be talking about these days, how first party data can be really used to leverage your understanding of consumer or customer behavior across both devices and channels. And we’re also alongside that, you know, really be deep diving into how we develop this person centric view versus a device centric or channel centric view of customer behavior. Couple of quick housekeeping notes. The session will be recorded so you will have access to this after today’s session. And if you have any questions during the presentation, please feel free to pop them into the chat. Amongst the participants today, we have an Adobe colleague of mine who will be monitoring the chat and answering any questions. As we move through the presentation, we do also have time at the end for Q&A if you have any additional questions which have not been answered. So without much further ado, let’s begin today’s session. So just a quick overview of today’s agenda. I will be starting with a customer experience across channels and devices overview. So this is just to give some context to the landscape today when we talk about that customer experience across multi channels and multiple devices. And then we’ll dive a bit into the two solutions themselves into cost advice analytics and customer journey analytics. I’ll finish up with some key takeaways from the session and then also point you towards some resources if you are interested in learning more about the topics today conscious that we have a limited time. So there may be that some of you in the audience who want to look into that in a bit more detail. And as I mentioned, also a Q&A as well if we don’t have all questions answered in the chat. Okay, so we’ll get started then with the first part on the customer experience across channels and devices and what that kind of looks like today. So this very, the slightly hectic slide is opening up the presentation today because it really speaks to the changes that we’ve seen in the last couple of decades really in terms of how customers today and how individuals really are working towards using multiple devices and whether that’s when they’re consuming products or whether they’re just engaging with a brand. And also in terms of that engagement, the multitude and plethora of ways in which customers are engaging today has really moved beyond calls and emails into things such as chat bots. We have call centres obviously which have always been in place, but we have additional things such as voice call. You know, people are not shy about going online. It is satisfaction with customers, sorry, with brands today as well. So with that has come an explosion in data, which many brands struggling to really gather together in a meaningful way to understand their customers as they are operating and moving across all of these channels and devices. Customers’ expectations have really been raised in terms of what they expect from brands when they’re on these different channels and devices. Customers are, you know, they’re not particularly aligned to a particular channel. They really, their expectations are aligned at the brand level. So they’re not fixated with a brand because they had a great email experience if that great experience is not replicated when they go onto a website.

It’s consistent and is meaningful regardless of how they interact with that brand. And when we think about this, you know, this idea of forces, the forces that are driving this change, we can kind of pull out three key areas which have really come to mind when we think about this explosion in data and how customer and consumer behaviour has changed. The first is this rise of consumer privacy and dependence and resulting dependence for many brands on first party data. Secondly is brands are now having to increase their focus on customer retention and lifetime value. And thirdly, you know, this demand from consumers for real time experience optimisation. And of course, all of this is taken place as part of this omni-channel journey as customers are moving across multiple channels. So if you take that first factor that I mentioned, this rise in privacy and we’ve seen here the rise of the first party cooking or first party data, I don’t think any one of the call is unaware of the fact that there have been massive changes in recent years in terms of third party cookie deprecation across multiple browsers. Google were slated to sort of end their third party cookies on Chrome this year. That’s been pushed forward to 2023. But, you know, regardless of when it’s happening, it is happening. We’ve seen with apps, obviously, Apple now require user consent to track and target users by IDFA. It’s no longer available by default. Customers have to agree to it. Many don’t. Now they actually see the message pop up. You know, there’s a lot of, oh, well, I didn’t realise that was happening before. No, I don’t want to be tracked. And many, you know, programmatic players are still using the third party cookie, but that will have to change as time goes on. And they are going to have to make some changes to their approaches and really evolve their strategy in terms of how they target and measure customers. Secondly, you know, the customer retention and lifetime value. So this is something that brands are really focused on, understandably. And in order for them to really bring that to life in terms of retaining customers and increasing average order value, they have to unify that plethora of customer data that I referred to in the first slide across the channels and devices into a single application where possible. So that enables anyone within the organisation to identify those opportunities to act as quickly as possible. The siloed data is not really going to work where you’re trying to ensure that customers have a consistent experience and then follow on from that is when they do have that consistent experience, you’re able to then retain that custom and ensure that you have customers for a period of time. And thirdly, as I mentioned, customers are, you know, expectation is to have this real time experience optimisation. Everything is about the now and people are no longer content to wait. If we think about, you know, brands such as Uber, once upon a time waiting for a taxi or mini cab for 20 minutes or half an hour was pretty much the norm. I’m old enough to remember calling up the, you know, mini cabs to pick me up for a night out. Now when you order an Uber, if it’s longer than five minutes, there’s a meltdown and pretty much ensues. So with customers, the real time experience optimisation is something that they are expecting. And in order for brands to do that, they have to make sure that they make those data insights accessible to everyone within the organisation’s depopulisation of data. They have to see that customer in a journey context sequentially across multiple channels and devices and understand that. And then linked to the first point is this harnessing this power of data science for this citizen data scientist, a relatively kind of new persona. But it speaks to the fact that you within an organisation, you have marketers and analysts who are not data scientists. They don’t have training in a lot of the high sort of techy skills, but they still need to be able to go into a platform and pull insights really quickly and not over the course of a few days or weeks. So in today’s webinar, we’re going to be talking about a couple of approaches to help you strengthen that identity, that first party identity strategy and help move you to understanding your customers across the devices and the channels as well. We’ll be looking at these two applications, as I mentioned, customer journey analytics and customer device analytics and how with the use of durable identifiers, we’re able to track cross domain activity and also cross device activity as customers move along. CJA is part CCA customer device analytics is part and parcel of Adobe Analytics for those of you who use use analytics today and CJA is a more sort of advanced feature or application which sits with within AEP. But we’ll look about those both of those two in more detail. So actually going to start with cross device analytics. It was the second of the two solutions there. But we’re going to kick off by looking at that first. So cross device analytics, as I mentioned, allows you to view customers as they move across one or more devices. And so it enables you to move from a device centric view of your customers or device centric view of whoever may be on your website to a person based one. And so as users move across different devices, as they determine and connect by logging in, by authenticating with an identifier, you’re able to identify that that journey across the across your properties as they use those different devices. There’s very much a focus on privacy there and first party data. So you can use CDA and identify your users with a private device graph, which essentially is a repository of identifiers that you have for your customers and stitch that across your devices for that person centric view. And it really enables you to capture accurate audience reach and in a simplified way. On the right hand side there, you’ll see there’s a little snapshot of the sort of configuration of CDA. I won’t be going into that in this webinar. It’s beyond the scope of today’s session, but I will be sharing resources at the end which speak to how you can set up CDA within analytics. It’s a very straightforward process and it’s applied at the report suite level. Essentially you need a report suite that contains data from both web app within a single report suite in order for you to enable to access that. So cross device analytics then enables you to answer questions about how many people performed a particular action versus how many devices. So we can think about how many people bought a product versus how many devices did so and how many people visited a home page or carried out some other type of activity or completed a success metric. And so the key to CDA then is this concept of people and the people metric essentially replaces what in analytics we have today is the unique visitor metric. So if we think about an example here where we see three different devices that were involved in this purchase, the overall revenue for this was 30 pounds. Depending on the way that you have analytics implemented, each of those devices may be reported as a unique visitor and so the 30 pounds revenue is then split across those three different devices. So you have 10 pounds per device. Whereas the people metric, which identifies that it’s the same person who was on those three devices, would attribute it to one person as opposed to splitting it across the three. So this gives you a much more accurate view of who’s actually completed that purchase and is responsible for that revenue. And so this just speaks to the fact that device-based analytics really does fracture that cross-device journey as I alluded to in the previous slide. Here we see a journey that begins with a search ad, then moves to an ad click through on a different device on a desktop, then an email click through, perhaps the customers received an email prompting them to go back to site on a desktop, then back to the laptop, then finally through to the first device again where they saw that search ad to carry out the purchase. Now if we think about channel attribution there and if we adopt a very simplistic attribution model and say, well, let’s think about applying last touch attribution to that particular journey so that we can attribute the revenue to a particular channel there. Then that search campaign which began on device number one was the final device was at device number one where the purchase was made. When we have this visitor-based analytics model, the revenue there is attributed to search because search was on that device that the customer was using when they completed the purchase. When we move to a more cross-device analytics view, the revenue is actually attributed to email. The reason for that is that email was actually the last channel that the customer was on before they purchased on that device number three. They did then go back to the desktop, they did go back to the mobile, but that was for the product view and then they obviously finally carried out the purchase on the device number one, but the actual final channel was on that third device. Because we’re able to recognize that it’s the same person across those three devices, we’re able to attribute that revenue to the correct channel because we’ve seen that it’s not three different people carrying out this journey but one. What we’ll do now is just have a look in the UI and maybe bring this to life a little bit in terms of looking at some of the reports that we’re able to generate when we are using cross-device analytics. Please bear with me.

Hoping that screen is visible.

I’m hoping that you can see the workspace for Adobe Analytics. As I mentioned before, cross-device analytics is accessible within Analytics. There’s actually a pre-built template for it, so if you’re familiar with creating projects within Analytics, under the projects tab you can select cross-device analytics as a template which will bring up this screen for you. As I mentioned, this is enabled within admin settings under the creation of a report suite which needs to contain both your web and app data. This is very useful in that it does bring up for you a list of the different types of information that you’re able to generate using cross-device analytics. I have some pre-built information here just in the interest of time and I’ll just call out a few of the insights that you’re able to access using cross-device analytics. We talked about this concept of people and so you’re able to generate reports that show you the number of people that you’ve seen across a given time frame on your property versus the devices. You’re able to again select people that have identified on one device or more than one.

So different devices with an identifier. Under here we can see this is the freeform table that’s enabled that visualization to be populated. We have the types of device and then the number of people that are identified across those different ones. Other refers to the desktop computer. We have people and unique devices and again you can select from the segment here filter people with one or two, people who you just use mobile, people who use desktop. So you’re able to get a really good understanding of the types of devices that your site visitors are using, how many are on two or more and this in turn enables you to carry out further insights in terms of well, what happens where those people have more than one device? Where do we see device type overlaps? So how many of those people are simply desktop users? How many are phone? How many are tablets? And where that overlap is between those three different devices and those individuals. So you can create a Venn diagram as you can for any of your analytics information in general. And again create segments out of this. You may want to target users who you know are on the phone and tablets but are not on desktops for a particular campaign. So generating this kind of insight enables you to do that fairly quickly. And here we have the cross device journey. Bring down a purchase funnel using different devices. We’re able to identify that with the flow path analysis. So this is super useful and where we know that from research that people behave differently on different devices. Often times when it comes to purchasing it’s not uncommon for someone to browse a product on a mobile perhaps, you know, in the morning on a journey to work but then move to a different device when it comes to actually purchasing that product later on, perhaps, you know, when they return home. And so this gives you a good understanding of that kind of flow as people are moving from different types of devices. And also in terms of the device fallout. So where you want to understand in that journey where people are starting at a certain point on one device and that are not actually completing their orders or where they do complete them. What does that look like in terms of the device journey? So we have a cohort of people here, you know, 260,000 people. And then we can see this path where they move from mobile then to desktop and then a very small number of those people, 1.9%, then actually go on to finally order something. But we’re able to see this drop off here, which tells us where people are dropping off in terms of the devices that they have. And then we have the cohort analysis. So a cohort analysis takes you, they wish you to take a group of people with very similar characteristics who’ve interacted with your product, with your property or product over time. And this shows an example of people who started on a mobile interaction and then eventually returned on a desktop device over a period of time. And the final section here, the four weeks, this tells us the overall percentage of people from that original cohort of just over 22,000 people who interacted with mobile, then went on the desktop. How many of them did we still see after one week, two, three and four weeks? So again, really powerful in terms of understanding, you know, when you have a group of people who share the characteristics, what does that look like week on week? But they’re carrying out that same type of behaviour. And then finally, we have here some analysis around attribution in a cross-device context as well. And so with attribution IQ within analytics, obviously you can apply multiple attribution models from first touch to last touch to multi-touch. Here we’ve just got a couple of models here, first touch and participation. And we’re looking here at how that revenue looks like for each of these steps in the purchase based on those different attribution models in a cross-device context. So there’s some really powerful reports and analysis that you can carry out within analysis workspace where you have cross-device analytics enabled. Just move along. So I’m going to move now to the second part of this presentation to talk about customer journey analytics, which is the second application that we’re looking at today. And customer journey analytics is essentially it’s an application service here that it’s built on AEP. And if you are an analytics user, you’ll be familiar with the interface, which I will share with you slowly, shortly, because it’s essentially analysis workspace built on top of AEP. So what this means is that you have the flexibility that you find within analysis workspace, that drag and drop functionality, which enables you to pull in dimensions and metrics and pull out really good, strong insights for analysis purposes. But on top of that, what you’re able to do is bring in data sets from AEP, Adobe Experience platform, which enables you to bring together both online and offline data. So analytics gives you that really good insight into your web behaviour. And with AEP, you’re able to bring in additional data sets from channels such as call centre or point of sale data. And within those data sets, if you have a durable identifier for your customers, site visitors or your consumers, you’re able to link their behaviour across the different channels, as long as we have that identifier across each data set. And to give you some really important and exciting visualisations into how your customers are moving across all those different channels so that you can help you to understand customers who are calling a call centre, for example, to either complain or discuss a particular subject. What does that then look like subsequently downstream? How many of those customers who call the call centre about subject X then go on to make a purchase or don’t, for example? So you’re able to tie all those interaction events together and then visualise that within analysis workspace with those additional data sets. There are some great sort of value levers really for CJA. Most importantly, I think, is the quick time to value. So many of our customers struggle to get the cross-channel information they require because they have to extract it from one platform and then maybe put it in another and then have a data analyst team go through that data and come up with reports, which are then shared with the organisation. But nobody’s really quite sure how they came to those conclusions. Well, with CJA, it’s very accessible in that anyone can go in. It really, again, democratises that data for the organisation. You’re able to get those deep on the channel insights and it’s easy for non-analysts to get that information as well. And then you’re able to layer on top of your segmentation some really, really strong AI and ML level on top of the segments that you build. So within analytics, for example, you have a concept of segment IQ, but you can look at how two distinct audiences are behaving. And with customer journey analytics, you can take that to another level by leveraging AI on top of that and additional ML. So some of the common use cases that we see for CJA from customers who are using it today are things such as call centre deflection. And we know from talking to many customers that call centre calls can be extremely expensive and oftentimes, you know, many customers may be calling about a particular issue across a particular locality or about a particular product multiple times. With call centre deflection as a use case, you’re able to stitch data that comes in through a call centre together with an online profile as well. So a call centre operative can pull up a customer profile in real time and within the call centre, look at the context of that customer’s journey across perhaps the web, or maybe they’ve been in store and use that to really improve that customer experience when they’re on the call. Click to bricks, so understanding of products are purchased online, what happens when they’re picked up in the store or returned to the store to get a really good understanding of that journey as well. Augmented analysis, so I alluded to this in the previous slide, but using the capabilities that we are already aware of today in analysis workspace such as segment compare or contribution analysis and leveraging those AI capabilities on top of that. And finally, overcoming some of the data caps that are currently a feature of analytics, so limitations around the number of EVAs and props, for example, it’s worth pointing out that that concept of EVAs and props doesn’t currently, doesn’t exist in CJA. And, you know, those EVAs and props that you have analytics come through as dimensions and metrics there. So you’re not restricted in terms of a limit or around cardinality as well. So that is helpful for those customers that really want to overcome those limitations. And on that note, so this is not the totality of the additional value that CJA brings, but just some of the key ones there, expanding the concept of the identity beyond the experience cloud ID. For those of you who are not aware, the ECID is that first party cookie that we drop, that Adobe drops when we have a customer or site visitor who’s in an anonymous state. So we can use that ECID to begin building out a profile of the person who’s on your property. However, with CJA, the key thing really is this identifier that you can use for your customers or site visitors, which goes way beyond this first party cookie from Adobe. It can be any identifier really that is currently apparent for your customers. So this could be a customer ID or some other type of identifier that you’re able to link your customer to across multiple datasets, such as call center, in addition to your online data. I mentioned before this unlimited variables and events, so no more worries or concerns about limitations on EVAs and props, which do not exist within CJA. And cross-report suite data collection. So I mentioned earlier for cross-device analytics that ideally you need a single report suite in which you’re capturing both your web and your app data. For CJA, your data can sit in multiple report suites. It doesn’t have to be consolidated into one. It can be across many, but you’re able to report across the different report suites, which is great for many of our customers who do have data split out amongst those different report suites. So on that note, I’m also now going to just jump into the UI for customer journey analytics to again bring some of that into life. If you would just bear with me, I’m going to log into a slightly different UI for that. This up. Share my screen. This takes a couple of seconds to load. Just bear with me. Hopefully this will load very shortly. Welcome to the world of live demos. While that’s loading. So the UI that you will will shortly see. And we go. Actually, this is bear with me. This is not the correct one. The UI that I will be showing you very shortly is, as you’ll see, very similar to analytics. And this again is, as I mentioned, because essentially CJA is analytics built on top of AEP. So what you’re able to do is leverage all the benefits of analytics in addition to the data that you are able to bring into via Adobe Experience platform. I’m hoping this. UI experience. And I’m just. On the. Unfortunately, I’m unable to bring that up. I’m very sorry. It’s there seems to be a slight issue with the UI today. So I’m unable to bring up the. I’m just going to give it one more try. Just bear with me. I believe I do have it now. Yes, I think I do. So I will. My screen. My apologies for. That delay. OK. Right. My apologies. So, as I mentioned, the UI, the interface, as you’ll see, if you’re familiar with analytics, very similar in terms of the different types of components that you’re able to use to build out a project. On the left hand, you’ll see the dimensions and metrics, which you’ll know about already from analytics. However, within CJA, you do have all these additional types of data sets that come in from experience platform. So if I just click on here to give you a quick overview of that, you have things such as the top pick, the call center ID. This speaks to those additional offline data sets I mentioned that you can bring in. Similarly, for metrics, you’ll see some familiar ones such as revenue, but then also some additional ones which you can capture. Where, for example, a customer is purchasing something in store using a coupon, or they could also be calling up to use that coupon and other metrics that speak more to an offline experience versus one on your website property. At the top here, you’ll see some additional tabs, which are ones that you won’t be familiar with if you’ve only used analytics. You have connections here, which really serve as a link between data sets that come into AEP and then feed into CJA. And within each connection, you’re able to view data sets. And these are the types of offline data and online that you can bring into platform. You also have data views, and these are broadly similar to report suites or more accurately, the virtual report suites in that they give you a filtered view of a particular set of data that you have. And within a data view, you can apply things such as a specific time zone or specific attribution model. So you can really customise the data view that you set up as it relates to a particular set of data that you want to view. And so again, in the interest of time, in particular, as I lost a few minutes there bringing this up, we have some pre-built reports here which show the ways in which you can bring in some additional insights when you have that offline and online data within Customer Journey Analytics. So we have some revenue figures here, which are fairly standard. But you can see here that you’re able to bring in revenue numbers from the call centre along with install and mobile and web as well. So you’re able again to capture that information from both your online and your online sources. We have this call centre engagement piece here where you’re able to bring in the information about the subjects of those call centre engagements. Why are people calling? And then this gives way to further analysis in terms of understanding what is driving the level of calls that we’re getting. And again, how can we optimise that and make it better for our customers? We can see the payment queries coming up top, but others around feedback and orders and deliveries as well. And then that journey flow piece again, it’s really important to understand how your customers are moving between different channels. You have your entry campaign and then again, as we can see here, the flow which shows how people are moving perhaps from an online channel to maybe going into a store. And then how that ends in terms of any purchases as well. And the cross channel overlap piece as well also gives you a really good understanding of how individuals are moving, not just across your different channels, but the overlap between them as well. So people who are on the mobile app, how many of them overlap with people who subsequently or who also, I should say, go into a store and also are on a channel. So there’s a real wealth of information here that can be brought in to really get that good and deep understanding of the cross channel journey that your customers are travelling on, how they’re interacting with people within your organisation, whether that be via a chatbot or a call centre, and then further downstream, what happens subsequent to that, after that chatbot interaction, what are we seeing from customers in terms of how many of them are then going on to purchase online, how many of them are then going on to purchase in a store, etc. So very valuable insights that can be gleaned from that. And as you can see from this whistle stop tour, there is a lot of information to be gained from this. And so, again, as I mentioned at the end, I will point you towards the resources that take you through in much greater depth and give you that better understanding of how you can get these insights. The final piece I’ll just mention is, as I talked earlier about the similarities with analytics, again, you can apply segments which are referred to as filters within CJA to any of this data to further break down the information that you’re seeing. So, you know, we have these filters or segments that have already been created here around things like store visits and offline channels, and that can be applied to your data.

I will stop sharing there and just return to my presentation. That was a demo that was completed. So just to summarise then really some of the key takeaways from those two applications. There can be some sort of confusion between the two. There is, in some people’s minds, some overlap between the two. They just wanted to call out these insights really to show how the two are differentiated one from another, but how they ultimately both help you to get that really good and deep understanding of your customers journeys. Customer device analytics then gives you a really deep insight into the person based audience analysis and cross device attribution. And so you’re really getting a good understanding of how your customers multi device customer journey is kind of taking shape. And you’re able to then unify that web and app interaction from multiple devices into a single journey stream. So avoiding that fractured journey that I referred to earlier, where it seemed as if three different people were involved in a purchase when in fact it was just simply one. With customer journey analytics, slightly different in that you are able to access multi channel information and analysis via experience platforms. That data is coming in from those data sets that go into platform with a common identifier across the platforms that then enables you to analyse both non web and digital data spanning multiple channels. So you’re using all the capabilities of analysis workspace on that multi channel stitch data set. And you can ingest really any data set across different sets to allow any user to create some really rapid analysis reports. You can also, of course, import your analytics data. You know, the quickest way to do that is via a connector that goes straight into journey analytics. So if you’re an analytics customer today and bring that data in is is fairly straightforward. So I will just finish by bringing up some of the resources that I mentioned that you can access. And I really want to point you towards experience leak. For those of you aren’t familiar with it, this is a great resource which helps our customers today and people who are not familiar with our applications. It’s really a one stop shop, a unified place to learn, connect. You can post questions, get answers from other customers, as well as from Adobe members of staff who do moderate the chats as well. So you’re able to also develop personalized learning paths that give you either self-guided or instructor led training via videos, quick bite sized videos on across all the topics that I very briefly touched on today. It’s global, it’s in multiple languages and it’s accessible on mobile as well. So as you would expect from Adobe, it’s something that you can access across multiple devices in terms of what we covered today. And there’s a whole section devoted to customer journey analytics as well as customer device analytics. The customer journey analytics piece is split up into separate areas with, again, a wealth of bite sized videos that you can access as well. So that brings me to the end of the presentation. And I believe maybe some time for questions. Steve, I don’t know if you have any from the chat that you wanted to bring up. Well done, well explained.

Thank you. So I might have missed the first part, so there were no questions or. There are in fact no questions on the chat. OK, well, yeah, I will take that as a compliment that it was well explained. But if anyone on the line watching does have questions that they think of afterwards, please don’t hesitate to contact your Adobe contact or representative with any queries that can be sent through to me. And I’m happy to engage with you after the call. So on that note, I will say thank you very much for your time today and participating. As I mentioned, the recording will be available afterwards. So please don’t hesitate to rewatch and come through with any questions that come to your mind afterwards. And thank you very much.