Speaker 0: Hi there, and welcome to our marketing session on how to audit subscriber activity for marketing cloud journey builder. We’re very excited to be here today. But before we get started, we just would like to quickly introduce ourselves. My name is Claudia, and I’ve been in marketing over fifteen years. And my passions are all things digital, including marketing automation and Salesforce, which I first been introduced to back in 2012. Now as a marketing automation manager and center of excellence lead, I’m managing a team of eight consultants and help organizations drive growth with marketing data account engagement as well as strategy consulting. I’m also a certified instructor for marketing data account engagement and a sales was marketing champion for 2021. And I would like to pass on to my colleague Austin to introduce himself.

Speaker 1: Thanks, Claudia. Hi, everyone. I’m Austin. I’m a senior marketing automation consultant and center of excellence lead for MediaMonks. I’ve been a Salesforce consultant for three years, leading marketing automation implementations for SMEs right through to multinational organizations. And one of my day to day motivators is helping clients, uh, gain the most value out of their MarTech stack and finding solutions to some of their problems, which is essentially how we came up with the solution for today’s webinar.

Speaker 0: Great. Thank you, Austin. Before, again, we get started, a special thanks to all our sponsors here. Um, as you can see on the slide now, wouldn’t be possible without these guys. Thank you so much. Okay. So for the agenda today, um, starting off with the introduction, so you just did, and we would like to talk about the challenge we are facing. So how do you find that prospects have gone on a journey, especially when the journey is thirty days or older? The solution we came up with is creating a journey, um, log data extension to showcase email, um, send and click activity as well as well as following the pathway and steps a prospect has gone through by updating attributes in the data extension journey. Austin will then walk you through the solution with a mix of an interactive marketing cloud demo as well as some, um, query slide overviews. And lastly, we will show you some additional visual reporting opportunities. Okay. So the challenge in detail. So the journey builder, as we might know, the journey builder activity history only goes back thirty days. So as a result, the question would then become, how can we easily find their subscribers or has been in a journey, especially if it’s more than thirty days old? How can we, in an overview, understand email send and click activity as well as the path that a subscriber has gone down, um, past thirty days? So the solution we came up with is querying marketing cloud data views. Um, Salesforce marketing cloud has a list of data views that can be queried to build different data extensions of records that have entered the journey in journey builder. For this solution, we will be querying, um, a combination of percent, subscribers, click, journey activity, as well as journey data views. For those who’d like to learn a little bit more about data views, we’ve added a link on the bottom here on the slides which will be shared, um, for further reading. Usually, data views only showcase data was not yeah. It was just for this last six months. But if we are using an automation where we can add data, we basically are able to build them beyond six months. So in detail, um, the journey activity only goes back thirty days. You can see those thirty days of activity within the journey, um, but you have to click through each, um, activity individually to see it, which is very clunky. The journey history doesn’t show you the clicks. It does show the decision splits activity, but does not detail which pathway a contact has gone down. You can go into email studio to report on send and click data per email, but you have to go into each email to see each subscriber’s overall activity. And the all subscriber list again does show their subscriber activity history, but not just for one journey alone. So you can see you can find bits and paces, um, of reporting in the different areas, but we can’t report on the journey alone in one single view. We have to basically stitch it together from different places. And the solution Austin will present to you in a minute is showing you beyond thirty days of journey activity, including email sent, click, and journey path activity for all subscribers that enter a journey in one location, which basically is in one data extension. As a result, that then consolidated in one location, it can be transferred to your preferred, um, the eye visual the eye tool for visual representation, um, of your journey performance. So now I would like to pass on to Austin to take you through the, uh, demonstration of the solution.

Speaker 1: Thank you very much. So before we jump into the solution, I figured I might just spend some time within the marketing cloud platform just briefly walking over those out of the box limitations that Claudio just spoke about. Um, let me just swap screens. Jump into our marketingclouddemoorg here. So fingers crossed, you can now see our journey coming through. So while that’s popping up, uh, we’ve built out a journey here. Just to run you through a few of the limitations that Claudia spoke about, if we we’ve got an email send activity step here within the journey interface. If I click into the email step, I’m able to jump in and see exactly which contacts were sent the email, who opened it, and who clicked on them only within the the last thirty days. Now the limitation here again is that it is sorry. The last thirty days, um, you can’t go beyond that. The other limitation is if you wanted to see from a high level view, uh, subscribers engagement across multiple email sends, you may have noticed we had about four or five different emails sends in this journey. You’ll have to go into each respective email send activity in order to see which subscribers have been sent and engaged in the content itself. So a little bit limited there. You can jump into journey history in the top navigation here and find a specific contact. If you wanted to, um, understand one specific individual’s engagement across a journey. The good thing about this is it does show you when they reach a decision split. However, the limitations here being that it doesn’t show you what pathway they went down once they reach or evaluated are evaluated on the decision split, and it also doesn’t show you any click engagement in your email activities. Now if we just jump back into our presentation, Jumping into Email Studio on the other hand, if we were to click through to a specific email send in our Email Studio reports, you would, yes, be able to overcome the the thirty day time frame limitation that we have within Journey Builder. Uh, but, again, you’re still facing that issue of having to go into each email sent in order to see a subscriber’s activity for a specific journey. You can jump into the all subscribers list and visualize what a subscriber’s done or the activities that have been performed for that subscriber over a period of time. Issue being here that you cannot filter on a certain journey. So the subscribers received multiple email sends within a time frame or over a period of time. You’ll see all of it. You won’t be able to filter down for just one specific journey that you’ve launched. So now that we understand some of the limitations that we have within the Marketing Cloud platform to date, How do we create the solution to get that high level one stop shop view of what a subscriber’s performed within a journey? The solution we’re gonna show you is kind of broken up into three different components. The first one is querying email send activity within a journey, email sending being one of the more common activities that are performed within Journey Builder. Once you’ve then got that query up and running, we can then build on top of that and add in our click activity to understand subscribers that are engaged in our email content, um, and the specific links in which they’ve clicked across that journey to. And then lastly, if you have any key milestones within your journey that a subscriber could reach and you would like to call that out, we’ll we’ll show you how you can add in any non sending activity within a journey as well, uh, so that you can put that into your into your table to report on as well and audit. For the email send activity. Now before we jump into the email send activity, you saw our journey builder campaign that we’re just showing you before to give you a bit of a context around what we’ve built out for this webinar. Uh, we’ve essentially built a journey for to capture attendees to this session. So you can see we’ve got our data extension dropping records into the journey. We then have a decision split that is listening for whether someone has attended the session or did not attend the session. Fingers crossed, we don’t have anyone going down that did not attend, and everyone’s on the attended session. Um, for those that do go down that did not attend, though, we’ve then got a further decision split down the pathway to listen for whether the subscriber is qualified or not qualified. And if they are not qualified, we are then up performing an update, which we’ll talk in detail a little bit later, but then we’re exiting from the journey. If they are qualified, we are joining back up to the top pathway where they’re continuing on as if they attended the journey. So that’s just a little bit of context around the journey that we’ll be demoing today. Take, um, just a quick note on the naming conventions that we’re using in the email sends because that will come into play shortly. Okay. So the solution for our step one, which is querying email send activities. As Claudia mentioned, we’re pulling together a whole heap of different, uh, data views. So in the data views, you can see we are pulling through the sent data view. We are then left joining on subscribers to grab the email address of the subscriber. We are then joining on journey activity so that we can grab the activity step within the journey that we are listening for. We are then joining on journey to be able to filter by the journey’s name. So you can see within the actual query itself, we’ve got purple items lined up. So in purple, you can see at the very top, we’re listening for my dreaming twenty twenty two e d m one underscore one. That aligns with the act the email send activity name that we’re using within our journey. And so we’ve just included the same number of activity names was listening for within the journey to line up with the number of email sends we have as well. We also have a a last email send date, which is going to capture the last email that was sent within our journey. And further down, you can see we have another purple, uh, elements highlighted in purple, sorry, which is capturing our journey name and the version number. So that where clause is actually filtering our data views to only listen for activity within the specific journey that you wanna set this up for. So you would just change that to listen for your journey’s name. And if you wanted to listen for a specific version of your journey, if you’ve got multiple versions, you can call that out. Otherwise, you can just remove that version number as well to listen across different versions. Now couple of key call outs within the query itself. You may notice we’re using this max aggregate function here and a grouping by subscriber key and email address. The reason why we’re using this is if we were to run the query without the max aggregate functions or the groupings, you would essentially end up with one subscriber being listed multiple times, meaning being listed as multiple rows within a data extension. What we actually want is that subscriber to only be listed once. Instead, we then want each activity to be listed as a column in our data table. So by using the max aggregate function, we can consolidate all of those subscriber records into one row, and then we can update all of the different columns to listen for our different email send activities. So just to summarize, in order for you to apply this to your own query, all you would have to do is update the activity name to make sure it aligns with your email sending activity within your journey, and you would just need to update the where journey name clause to listen for your journey’s name. And as a result, we can now use the query we wrote in an automation within Automation Studio with the SQL activity, uh, to populate a data extension. Just a note on this, we need to create the data extension with all of the required columns, including ones we’ll be covering off in step two and step three of this solution before running the automation. And another little quirk is that we’ve set the SQL activity in the automation to update, not overwrite, so that we can add to the data extension and capture data beyond that six month threshold that data views allow for you to capture information for. Including click activity. Okay. So how do we build on our email sending query that we just built in the data table we’ve populated to also include subscribers click activity? So in order to do this, there’s a a few different ways you can add in the click activity. Um, just a note, the purpose of adding the send activity was so that we can ensure that subscribers are actually receiving email sends throughout the journey. The click activity is not so much an auditing tool from, let’s say, a journey, uh, performance perspective, but it’s more or less for reporting to understand is your content resonating with your subscribers. Is, um, uh, is the frequency of your email sends correct, that your subject line is optimized, um, things like that. So you can draw on those sorts of conclusions by understanding the level of engagement that subscribers are taking within your emails via whether or not they’re clicking on your primary call to actions. So as I mentioned, there are a few different ways you can go about adding your click activity to this solution. One of the options is you can listen for the link URL, which you can see in the top section of the screenshot we’ve got on the on the slides here. Um, however, the limitation with using the link URL is you may have that URL listed in multiple sections of your email, and you won’t be able to determine which section of the email they clicked on that specific link. So we’re opting to use the tracking alias instead. And the tracking alias you can see is has got the purple box around it in our slides. Tracking earliest will allow you to call out within an email which specific URL they clicked on. So was it the call to action, one, sitting higher up in your email, or was it something sitting much further down in your email also linking to the same URL? So now that we’ve updated all of our emails to add that tracking alias in, we can now listen for, um, the tracking alias link name in our updated query that we’ve got here. So you can see it’s very similar to our send activity. The only thing that we’re doing here is adding in the click data view. So you can see we’re left joining on, uh, clicks on subscriber key and job ID. Um, And we’re also now adding in the link names for the specific URLs we wanna listen for within our each email. We’re still using that max aggregate function so that we’re consolidating all of our records into one row. And as a result, it’s going to output a true false value. And now you can see in our data extension table here, we now have a column for our email sends, uh, email click for all of our emails across the journey. You can see we’ve got our subscriber key populating in our email address so we can work out specifically who is entering the journey, and then we can see their last send date within the journey too. So how long was it when since they last received an email within the journey? So how do we take this one step further and include any non sending, uh, non email sending activity within our journey? So within the journey builder activity itself uh, sorry. Within the journey builder itself, you can add an update activity step to your journey in order to capture when a subscriber reaches a key or critical stage in a journey that is not email sent related. In this example, we are updating an exist the existing data extension we just showed you to capture when someone reaches the not qualified pathway, um, and as a result, has exited the journey. In this scenario, we wanna capture when they’ve reached that not qualified pathway so that when we’re looking at our data extension and they haven’t been sent the MaDREAMING 2022 EDM two or the my dreaming twenty twenty two EDM three, we can identify, well, it’s because they are not qualified and they’ve gone down that bottom pathway and exited the journey. So it gives us some sort of logic and reasoning behind why they were only sent one email in our journey. Now just a couple of call outs before I I go into this a little bit more detail. Um, in order to use the up update activity step in your logging data extension, The data extension you’re updating needs to be sendable. The record that you’re updating in the data extension also needs to exist in the data extension prior to the update. Now if you wanted to capture when someone reaches a certain stage of a data of of a journey, sorry, and that occurs before they’re ever sent an email, there are a few different ways you can update that logging data extension to make sure the record exists. Um, I won’t cover that often today’s session just due to time constraints. Um, but if you do have any questions around that or would like to explore that further, please feel free to reach out to me. I’m happy to walk you through a couple of those options. But now you can see we have a data table with our journey activity detailed. It’s a great one stop shop for working out where a subscriber is within your journey, the pathways they’ve gone down, and the activities in which they’ve been sent or engaged in. Additionally, you can now use this data extension table to export into a BI tool if you would like to visualize any of the activity from this journey too. We’ll show you that in just a second. Um, what I wanted to just quickly stress here is that we’ve been focusing on your email sending and your the journey pathways from an update perspective. You can also build in SMS if you would like into the logging data extension if you would like to capture SMS tracking. Uh, there is an SMS message tracking data view, um, that’s available. You can see that through the link that CloudHQ had in the slides prior. Um, it is widely accessible through Salesforce’s documentation. So have a read through that and just check out if there are any additional things you would like to add to your your logging data extension. Um, we’ve just been focusing on email because that’s quite a common scenario for most people. Okay. Visualizing your journey activity. So because we now have that data table that we can use for auditing subscriber activity and visualizing the emails that have been sent, clicked on, on the in the pathways they’ve gone down, we can now export that data table and plug it into a BI tool if we would like to visualize that. Now I understand that some tools allow you to ingest these data views as a whole to visualize journeys such as Datorama, for example. Uh, but for those of you that don’t have Datorama as a tool in your MarTech stack or you would like to visualize, say, non sending activity too, this is a dashboard that one of our colleagues has built, uh, for us purely for this demo, uh, using Google Data Studio or or Looker Studio as it’s now called. Um, and by having all of that data sitting in a data extension ready to go, you as the marketer can have full control over how you visualize your journey activity and reporting, um, dashboards. So in this example, you can see we’ve got our counts for all of our send activities, our clicks per email as well. We can pop that into a graph or a bar chart if you would like to visualize it there. We’ve got our unqualified exit pathway count too. So if you’re getting a lot of people going down a specific exit pathway, you may want to work out whether to change your journey structure to ensure that people aren’t exiting too early or not going down their required pathways. And then we also have account here to understand the average number of clicks that a subscriber is taking within a journey and which subscribers are highly engaged within the email. So you can see that on the far right where we’ve got our nine subscribers there clicking on on links. So it’s a really good way for not only auditing your subscriber activity within the journey, but visualizing their engagement within the journey too. That’s the end of my section. I might hand it back over to Claudia to wrap things up.

Speaker 0: Thank you very much, Austin. Excuse me. Thank you very much, Austin. That was awesome. Um, great, um, yeah, great presentation of how we can put all of our reporting in it from a journey into one data extension. Um, Yeah. And then we can refer back and check where our records are within the journey at any point in time. And if any of those records are not reaching the path that you would like them to go down to, we can always go back and see what has happened to them and why they have been why they have veered off the path. So, um, yeah, we’ve also seen that there’s, um, a few questions came through in the chat. Um, we will definitely get back to you guys, so don’t worry about that. And we would like to thank you very much, um, to, yeah, come and see our presentation today. We hope you enjoyed it. You can also find both our email addresses and LinkedIn links, um, on the slide you’re seeing now. Um, feel free to reach out if you do have any questions, but, yeah, we will also come back to you and answer the questions which came through in the chat. Thank you.