蜜豆视频

Transcript

Hello, everyone. Good morning or good afternoon? We鈥檒l just wait a couple of minutes until we get started.

Today鈥檚 session will be focus on mastering sequential logic and analytics and CGA. And it鈥檚 led by Andy Powers. We鈥檙e going just to wait another minute for attendance to filter in, and then we鈥檒l get started. While we wait, we can comment on the chat where people are located, company and what they鈥檙e hoping to get out of this session.

So as we鈥檙e waiting to filter in, I want to let you know that we have several other sessions coming up. This quarter that are open for you all to attend as well.

For those who are interested, I will put the links in the sessions of the session. Sorry.

The owner to 22nd. We have work. Work from mastery strategies for success. Success and basics. Introduction and am authority. Key concepts and capabilities. On October 29th, turning leads into loyal customers. And on November 11th, we have unlocking the power of 蜜豆视频 Gen Studio. Organizational readiness to address content supply chain challenges.

So I鈥檒l give this time over to Andy so he can start his, session today. All right. Thank you, Camila. And thank you everyone for joining. I think that everyone is muted by default in this presentation. But if you have questions, feel free to put them in the Q&A, section of teams. Now, I鈥檒l comment on the minute, about questions. I don鈥檛 anticipate there being a lot of time for Q&A today because the content is, dense and complicated, and there鈥檚 a lot I want to go through. But let me get to that in a minute.

So I kind of given this a subheading of foundations today, because there鈥檚 a lot that you can do in analytics and CJA with sequential segmentation or sequential filtering. Today鈥檚 goal is to go through some of the more introductory aspects of this feature. Make sure everyone has a grounding on how they work. And then that will give you the foundation to take these insights and processes back into your organization and your data sets and analytics of CJA, and practice and learn them more.

We鈥檙e going to look at the then operator how after and within modifiers work. And then also, explore a bit of the only before and only after sequence options in the sequential logic module. For each of these, I鈥檓 going to try to introduce them with some quick sample from the UI itself.

Business questions that this feature can help you answer. Usually some sorts of notes or descriptions on the content or how it functions, and then most of the time will be going straight into the tool to give demos and demo interpretations.

So like I said, today鈥檚 objective is what I鈥檓 calling a foundational understanding of how these work, just because there鈥檚 a lot to it, and it鈥檚 a hard topic to master without practicing in your own environment. So we鈥檙e going to look at the operators that I mentioned. We鈥檙e going to look at example use cases and also talk about how you can validate that you鈥檝e got a definition, properly configured. And again the expectation for today should be not that you have a grasp of every concept and every nuance of how it works, but that you have a foundational understanding of how to input and implement and interpret the logic. And again, like I said, I don鈥檛 expect a lot of Q&A time for today because the topic is fairly complex and it would be hard to both kind of maintain the flow of ideas and jump around between questions. But whatever questions we do get, if we don鈥檛 have time to cover them here today, we鈥檒l try to follow up with them, in a subsequent, session or email. I鈥檓 hoping that this can be a series because like I said, there鈥檚 a lot to cover, so it requires a lot of guidance and practice to master these. But these are some of the most valuable and powerful techniques that I think our analytic solutions offer, especially in customer journey analytics, where you鈥檙e always talking about looking at different data sets and how customers take actions over time. The idea of sequences and how to define and analyze those is paramount. So I鈥檓 hoping that this will be a series. And I鈥檇 love if you give feedback in the pool at the end. If you鈥檇 like to see more. Yes, go into the more advanced topics. Couple other, preface comments. So I need to assume that everyone has at least some familiarity with analysis workspace. Whether it鈥檚 an analytics or CJA, that doesn鈥檛 matter. The the interface and the way that we define sequential segments or sequential filters is identical. And I鈥檓 hoping that everyone has just some basic familiarity with the aspects of how, at least the non-sequential elements of a segment definition would work. So things like, excuse me, how the container level, whether we鈥檙e looking at person or visitor level versus, event or hit level, how that influences interpretation, just the basic functions of Ands and ORS. And then also the idea of simple exclude include as long as you have some context for how that works, that that鈥檚 all I鈥檓 hoping for because I鈥檓 going to start from there so we can focus on the sequential operators rather than a a start to finish of how segments work. And then the basics, since our audience is covering, users from both the analytics and CJA, naturally we have changed some terminology between the two tools, but the functionality is identical. I鈥檓 going to try to use, words from both tools, and I鈥檓 going to do it interchangeably, in part just because I鈥檓 accustomed to doing it. And I will forget. But just keep in mind that in analytics, the structure we had defined it鈥檚 labels were visitor visit and hit. In customer journey analytics, it鈥檚 the same idea, just uplevel because we鈥檙e usually able to identify our customers and we talk about them as person, session or event. So any time I use these terms, we鈥檙e just talking about, you know, top level way to identify someone, way to identify some period of activity and way to identify individual hit or event or records.

And then lastly, in analytics this whole feature is called segmentation. In customer journey analytics it鈥檚 called filtering. Just because segmentation has some other contexts. So those are the terms will be working with today.

Now let鈥檚 get started. And let鈥檚 look at the the basics of how then works.

So whenever we use the then modifier all we鈥檙e saying is whatever is before and after that, then I need to see that these happen and that they happen in this order. So what I have here is a simple example. I want to look at sessions, and I want to know that there was some engagement with a web channel, some engagement with email, and that web occurred before email within the session.

In both tools, all of your data is grouped by the visitor or person level and then ordered by timestamps. So when we have then here it鈥檚 like it鈥檚 introducing a timestamp check in addition to what you see actually listed here as our check for web and email. As you know, our dimension conditions.

The basic implementation of then just requires that these happened on separate hits, separate records of data. So if I had something where I could, on the same page view, for instance, trigger both my before and after checkpoint, that would not match, then does require that these at least happen on separate hits, separate rows of data. And then lastly, just basic comment you won鈥檛 see. Then by default, if you only have one condition, you鈥檒l only see and and, or. But once you have to then then will come as an option.

So these are the kinds of things that this lets you answer from a business value perspective, which customers purchased after engaging with our mobile app. Or I want to study the sessions where conversion a conversion occurred, but there was no prior marketing touch in that session. Or if we want to compare groups, we might look at the customers who say arrived in store, engage the mobile app and then purchased in-store and compare those to the ones who arrived in store, did not use mobile app and still purchased in store.

Two themes here one. We鈥檙e always thinking whenever the business question is about sequence or order after before, that鈥檚 going to be a logical time to use then, but also at its core, this is going to give us back visitor customer level data or session visit level data.

Some notes, another kind of intro concepts. And then we鈥檒l go right into the tool. All we need is that at least one piece of that visitor鈥檚 behavior in the session, or the visitor, kind of clickstream data matches the sequence, and then we get that whole container level back. So for instance, here we鈥檙e looking at sessions where we had web followed by email. It鈥檚 going to match something like web, email, web because I have web here. And then later in my visit I have email. But it鈥檚 also going to match something like this where I have I may have email before web, but that doesn鈥檛 matter. All that matters is that I have at least one time when my check point before the then is met, and at least one time where the check point after is met. So it鈥檚 not.

It doesn鈥檛 matter if there may be something that鈥檚 opposite order. All that matters is we have at least one time where it did match.

The top level container works just like it does in a normal segment. But one note since sequences require that this is on separate records of data, if you have this at include hit or event level, you鈥檙e just going to get back zeros in your preview. So if you鈥檙e ever trying to build something and you see zeros there, check this right away. Make sure you鈥檙e at session or visitor level.

The include everyone modifier here is where we get only after and only before. And it has dramatic impacts on the results. So we鈥檙e going to cover the basics of those today.

But you can just think of include everyone has the the default behavior. Whatever I match here, I鈥檓 just going to get back all my session data, all my visitor level data, the container levels of any conditions, they do matter and they influence the results. So in my screenshot here I have these as page view, event level conditions. But if I had these as visits for instance the then here would actually say these need to happen. This condition this checkpoint needs to happen in a separate visit from the next checkpoint. So the container level here matters. And yeah, that鈥檚 all I need to say on that.

And then the last place that we can interact and adjust our settings is here and within. And then, which is accessible via this clock. So here we could do something like, say, these touch points needed to happen within a three day period. But after there is a purchase and we鈥檒l cover, the after within and only after only before today. If you鈥檙e looking at the summary view, if you just click on the eye icon on a segment or filter here, I just wanted to point out all the information that鈥檚 conveyed here. But some of the icons are really small. So this include everyone only before. Only after. It鈥檚 it鈥檚 here in a tiny icon at the top. So just know that the information is there. But the icons, carry a lot of weight, especially if we鈥檝e change this to only before. Only after all. Right now I want to introduce some, for demo visitors that we鈥檙e going to be studying today. So what I did was, this is a demo data set, so it鈥檚 all fabricated. I found for visitors with some fairly short sessions of inactivity. I said, I just want to be no more than ten, ten hits, ten events, and I want to make sure that they touched these particular channels, these particular dimensions of interest, because this will give me something to study. As part of our session.

So here I鈥檓 looking at person A, I鈥檓 looking at their sessions or visits, and I鈥檝e adjusted this in my live demo to where you just see ones. But at the moment, this demo data in my screenshots showing, you know, it鈥檚 demo data there too. Identical visits here, but there鈥檒l be ones in our, screens here. So this person engaged two times on web within a session, then had some email engagements, maybe click through, open something on the mobile app, continued on to web for two more interactions, and then add three more in this session where they were engaged on the mobile app. Although I鈥檓 showing this as kind of channels in the customer journey analytics context, the idea is the same whether it鈥檚 page names, page views, any any dimension. I鈥檓 just using these because it鈥檚 easy to discuss the story together here. Person B has two sessions also, but we can see that sequel Instant Order. They happened on some different channels. One of them had web first, one had email first. The fifth record on each one was here in the web channel. And then it split off after that. And I think one of these ended on the ninth. One of these ended on the 10th. Person C starts all in web for four, hits an email, and then splits between email and mobile app and then concludes on the eighth hit.

And in the went in this cycle here.

This is for us to have examples to look at. But it鈥檚 also the same way. I鈥檇 recommend that you work with these concepts in your environment. You want to find some samples, validate what your definition means when you鈥檙e trying to work with the new segment criteria, and then iterate from there.

All right, let鈥檚 go to our demo.

So here are our four examples that I was just looking at.

The first thing I want to do is continue on to what I have here in this. And then panel, let me minimize my others.

All right. So here on the left I鈥檓 going to just be looking at, the original kind of definition for a person. And on the right we鈥檙e going to look at the impact of adding our segment where we had this in our example, our, web than email, web than email definition. And just to show you, it is the same, I鈥檝e just hidden the actual conditions. But sessions where we had web followed by email. And then one other thing I need to do.

All right. So here we can see we鈥檙e looking at person A at this point. And like I said I adjusted this to we鈥檙e only going to look at one session rather than having all the tools. So on the left person A went to web. And at some point in the session they went to email. So we would expect that our segment here sessions, web and email should return everything for this session. And that鈥檚 what we see here. The same result on the right means that that that was the returned, result.

Let鈥檚 switch over to our person B next.

This was the one that had two sessions where they sort of started on different sides, one on web, one on email, came together and web and then split off from there.

Only one of these sessions actually matches our condition. So if we look at this one we see web here. Then email. And that鈥檚 why this one came back. The other one was the one that started here. Email app. Went to web but then went here on this left branch and never came and touched email. So the result here is we鈥檙e only going to get the one that had web followed by email.

We look at person C here. Both of their sessions had a web interaction and both had an email afterward. So we get all of that data back when we apply our segment to them.

One other thing to I want to call out. So these concepts are simple sometimes and complex other times. So the reason that I emphasized that today we鈥檙e not going to do a lot of Q&A and that I want to kind of present this material, is that you can take the recording and the presentation materials back and really practice this on your own in your environment, and that is really the only way to to get very comfortable with all these concepts.

But let鈥檚 look once more at our B person here. So this person an email app, web app, web app, web app. And so when we were looking for web followed by email, there鈥檚 no email in any of the later activities of this session. So we don鈥檛 get anything back.

So that鈥檚 our introduction to then we鈥檙e going to roll on and move in to after and within. So this is the modifier that we open with the clock icon here. This is all about setting modifications to either the distance between the checkpoints or the proximity between the checkpoints. So here I can say within two events or hits. And what I鈥檓 going to get is I鈥檓 going to get all the sessions where I had web followed by mobile app, but the condition is that the mobile app had to happen within the two event window after web.

So we consider these, potential sessions customers could have. These would match. So if I had web and then the next hit was mobile app, that would match. That鈥檚 within two events. If I had web, then something else, then mobile, that would match as well. But here, if I have web, then two hits and then I don鈥檛 touch app till three hits later, this would not come back as the result. For this definition. After is kind of the kind of like an inverse of it. So here we鈥檙e going to say these two checkpoints have to be separated by one event in between. So here I have web with something in between and then app that will match I have web and then I have a lot of interactions. Other hits of data in between that would match. But because I have my after one event here, I would not match if the customer just went from web and then their next hit was on the mobile app.

And we can combine these. So usually when you combine them you鈥檙e going to get very specific. So here I change my level to a person or visitor level. I鈥檓 going to pull back person level data where both web and app occurred. And the web activity has to be followed by the app activity in the second day after web. The way you get that is it has to be after the first day. So now we鈥檙e looking at second or third or later, but it鈥檚 got to be within two days. So the only result is that this mobile app engagement had to be two days after web. And that of course, would be calculated accurately based on hours.

So, you know, if we looked at something between 25 and 47 hours afterward, that would match. But if we鈥檙e either shorter or longer than that frame, that result would not come back for our definition here.

So ways to think about after and within which customers purchased in the the two visits after they first launched our mobile app. Or I want to understand the sessions where the conversion occurred, but there is no marketing touch within the previous 30 days, for instance. Or maybe we want to compare customers who sign up and visit within the next three email opens they have, or after the next three email opens. Or what can I learn about sessions that are one visit exactly after listening to our podcast or our webinar? Whatever our data point is, I can look at this two visits later, three, etc.

the idea here is you鈥檙e looking at kind of a condition and note that there鈥檚 lots of ways you can define it. So we can look at things like a count of events or sessions, hits, visits, etc. and analytics terms. But we can also do things like look at period of time or any of our dimensions. So we could say I want three age name values later to see that some condition was met or I want to say that three days later this condition was met. The variety here gives you a lot of control over how you want to identify an answer or a business question. In my examples, we鈥檙e really just going to focus on this like hit level, event level kind of what was the next thing in their data set? Because that鈥檚 simple. And it鈥檚 but there鈥檚 a lot of flexibility here.

Just note that even if we say within excuse me, the checkpoints do still need to happen on separate records of data. So if I say within one day, or within one event, it still has to be like a separate record of data from the one I started on. And note, if you do something like after one day and within one day, that will give you no results, as you would expect.

All right. Let鈥檚 look at the demo for this.

All right. Here鈥檚 what we鈥檙e looking at. So again here I鈥檓 looking at person A. And on my left is the session that belongs to person A. Then I have a few different definitions to just give different ways of looking at this here I鈥檓 going to say my web touch. Let me show how it looks in the builder.

I need sessions where web was followed by mobile app, and mobile app came within the next two events. The next two hits or records of data. So here, if I look for here鈥檚 some of my web activity, I鈥檓 going to look at the next one. Two records of data. I don鈥檛 find any app data there, so it鈥檚 not going to match based on this part of the sequence. But if I look at these web interactions here and I look within the next two hits, I do have at least one time where web is followed within two hits by app data. And so I get back that whole session here.

In this one, I鈥檓 looking at the same idea, but instead of within I鈥檓 saying after one event. So here I have web and then 1 to 3 events later I have engagement with app. So this will return back to my session because I had at least one time that that condition was met. And on the last one here we鈥檙e looking at the combination of the two. I鈥檒l open that up.

Sessions where web is followed by mobile app. And again this can be anything. Could be pages viewed products. Added to cart whatever. Just using this because this is the context I chose for my demo.

The web mobile app need to be separated by one or more events, but needs to be no more than two events. So this is really going to give us when mobile app came exactly to events after engaging with web.

And so here I have web one to nothing matches for these two. So the system goes through and checks all the other hits that are web hits. And both of these will match the condition that one two hits later I have app. And so for this person A we see that session comes back for all of these definitions. Let鈥檚 look at person B. And really this is this is the way to do it. Finding some structure where we know, the data is simple, we know how to interpret it and what it is might use a segment definition to sort of kind of pull out just a little bit of data to do these validations on and then testing it. This is how how to start until you鈥檙e comfortable with exactly how it鈥檚 interpreted by the engine.

So here web and then app within two events here we鈥檙e matching one of these sessions. We have this event five where it was web. And then afterward this session it鈥檚 hard to see on this side. We鈥檙e not actually revealing what session one and two were. But you see it from the results here. This was the one that went from web to app and then had three more on web. So our condition is met. This is within two events of web. So this session comes back but the other session does not. The other session touched web and then went over to email and then app. But it was later than within two events here when we鈥檙e looking at the web and then need at least one event to separate them, this one matches that other session web. And then after one event at least we see app. So here this one is three events. So it matches. And then when we鈥檙e looking for this combination where it鈥檚 web and then two events later. Exactly. Neither of these matches here web two events. There was no mobile app. So it doesn鈥檛 match this one. And here web two events later doesn鈥檛 match that.

Person. See, just run through the same thing quickly.

So we match one of the sessions web. And then within two events we had app here on this one where we鈥檙e looking for at least one event separating the two. That鈥檚 actually matching both of the sessions we have here. So we had web and then one of them had a app interaction here. The other had an app interaction here. So both of those come back when we鈥檙e just looking for the one that has exactly two. Only this first session here matches going from web one to matching mobile app.

And let鈥檚 look at our last example person.

So this is the one that bounces between app and web a bunch. So we have web engagement looking within two events. We have app. So this session comes back for looking for web. And then after one event we see app happened later that session will come back. But as far as looking at it very strictly where we were doing after and within, we do not see web followed two hits later by app. Whether it鈥檚 for this or this or this instance of web, so we don鈥檛 match it for that session.

Go back to my back so I know it鈥檚 a lot of material. Again, the goal today is introducing a few foundation to practice more in your environment and give you some resources to help you validate and verify interpretations, because it鈥檚 it鈥檚 a lot to cover. It鈥檚 a lot to digest. So this is the one that originally said include everyone. It has two other options only before sequence and only after sequence. This gets very interesting because no longer are we getting back a session. This changes it so dramatically that what we鈥檙e getting back is just part of a session. In this case we鈥檙e saying return back just the subset of a session. So some number of hits of a session that occurred before and up to the point when we see this sequence of web ten app. And if that鈥檚 not clear so far, that鈥檚 okay. The examples are what helps a lot for this. We鈥檒l talk both about how this can be used very powerfully for business analysis, and then also how you can validate it and see it with examples like we have in their live demo today.

So the result here is not a session. And that鈥檚 why this is one why it鈥檚 tricky to use, but also why it鈥檚 very powerful. So when we look at this sequence here web than app, we鈥檙e just using this as a way to say if I find this I want to match everything that happened before that.

Once we go through examples, if you think of it as how you might try to implement this in a BI system with SQL compared to the control that you have here, just with a few modifications to a segment definition, it鈥檚 extremely simplified, although still takes time to just understand and know how to use. It鈥檚 so much easier than trying to build this as part of a complex query.

Here, let鈥檚 look at the only after side of this. So this is going to say if I ever see this sequence web followed by app, then pull back everything that is the subset that happens after or following that sequence. And a reminder if we just click that summary icon, this little blip here is the only indication you get that it鈥檚 only after sequence and has such a different meaning. So take care when you鈥檙e, looking at the the summary that you know what this icon is. If you鈥檙e using then operations.

And just to remind include everyone just think of it as, as the nothing like this is just the default state where we鈥檙e going to get back a whole session like normal. And we need to know that session had web then app, no partial sessions, no partial visitor data, just a regular complete result.

Okay, so let鈥檚 talk a little more about what you can do with this and why this is useful. Sometimes you want to know what customers did after, say, a mobile app installation or a campaign. Click through whatever. I鈥檓 just making examples here. If I tried to study this whole session with a mobile app, install in it, I鈥檓 going to see activities that happened before the mobile app engagement and after the mobile app installation. But this lets me just study the part of the session that I care about. What did they do after? So think of it like if someone click through on a paid search ad and I that鈥檚 poor example, but let鈥檚, let鈥檚 say someone is engaged on the site, then they go to search, search for something, come back, say that there鈥檚 just the use cases that I only want to know what they did after the thing of interest. That鈥檚 what this lets us do. I can pull this onto a table and then analyze only the pages that happened after. The thing that I expect may have changed my visitor behavior. It can be after they submitted a lead or before they submitted a lead. So I don鈥檛 get the noise of all the other activities. That will make it harder for me to understand the impact of this thing I want to pivot on. Or let鈥檚 say let鈥檚 look at a customer visitor level. What characterizes their activity before they鈥檝e ever engaged with our marketing. So if I applied this at a visitor or person level, I could just say, see where there鈥檚 a marketing touch and just give me everything that happened before that. And I can study this, and I won鈥檛 be confused by all the things that may have happened later when we鈥檝e influenced them. We can understand their behaviors if they鈥檙e not influenced, or we can say, what? What channels do a lapsed customer engage after canceling a service? If they come back, what else do they do? And again, if I just looked at customers who did cancel service, it would be hard for me to separate the before and the after activities. And that鈥檚 what this does or even what characterize this is the the portion of a session, after listening to a podcast or clicking an ad or another very common one is all right. What were the search terms? The the documentation viewed some sort of attempt for self help before they called in to support, because we may have a support person who鈥檚 guiding them to look through things online, on the phone, and that would make it hard to understand what the their attempts were before that. So this gives us a way to just see what they did before.

So examples will help a lot. Just two other general points. The logic tends to be greedy. So say we have multiple matches. It鈥檚 generally going to try to return the largest amount of data that matches. And I鈥檒l show you what I mean. And if you look at the API calls, this is just another way to think of it. The calls they are actually called like sequence suffix for only before sequence prefix. And that just may be another way to think of it that might help some of you. Can they internalize it better. So the idea is that give me all the data that ends with a certain suffix as some sequence that was met, or give me all the data beginning with some sequence prefix. In my behaviors.

All right, let鈥檚 hop into this one.

This will show why it鈥檚 so important to use this kind of coloring. And the examples that I鈥檓 doing. So here person A鈥檚 session web email app, web app here. Look at the definition I brought in.

Give me the sessions where web was followed by mobile app, but only give me the data, the portion of the session, the subset of data. Before I see this sequence. So let鈥檚 look for a sequence where we see web followed by app. Web starts here. Any of these and app occurs here. Web is followed by app at this point. So there鈥檚 a whole bunch of possible sequences here. Like I said, the the way this goes is as greedy as possible to give you the largest result back. So what we鈥檙e actually getting here is let鈥檚 look at this one. This is our last web followed by app sequence that matches.

We just want everything before that. So it鈥檚 giving us back the end of that sequence that we鈥檙e looking for that suffix which is this web hit. And it鈥檚 giving us back everything before that, if I were to look at it differently and just say, all right. This web hit was followed by app, give me back everything before this version of the sequence that would also match. But the the operation is going to look for the one that gives the largest result. And the largest result is found when we look at the the last web hit here, and we find the mobile app afterward. So note two things. One, we鈥檙e only getting back a portion of data, not a whole session. And it starts with the the ending of that sequence that we鈥檙e looking for. The ending of our suffix here.

Going through examples will help, I promise. If the if you鈥檙e having trouble following along because this is you of our customers, are familiar with this and use it, but it鈥檚 it鈥檚 one of the most powerful aspects of our tooling. So here the only after can be a little easier. I want to see web followed by app and just get everything after I see that sequence. So the biggest result is kind of probably be from this app hit here where I see a web followed by app. That鈥檚 the the sequence that will give me the largest result back. And so as soon as I hit the end of that sequence, I can get app, I get all the data afterward. So here for the business context, imagine that again this is this is a click on a campaign ad. This is a sign up for a lead, something that changes the visitor鈥檚 kind of relationship perspective. And you want to just study what happened before or after. This is just giving you the before after. So if I applied this on a table and I said, tell me the products this, segment looked at, I would only see anything that happened after the point of interest.

Let鈥檚 look at person B, and here I鈥檝e actually separated the segments a bit. Are the sessions a bit so we can see them more clearly? Has person B had two. But it鈥檚 hard to interpret that when you have both of those. So here I have a web hit followed by an app hit.

This is the A sequence that is getting matched. And then my condition says just give me everything before that. So starting from the end of the sequence, I get back everything before I opposite happens here. Because this is the only time in my session I have web followed by app. I鈥檓 going to start here with this hit six, where I matched that sequence and then get everything back after it. So that鈥檚 why we see App and the subsequent three web activities.

Do more examples.

And this will be the last of the kind of concepts that we鈥檙e going through today. But it鈥檚 the trickiest one. So the examples matter a lot. All right here I have web and I have app.

If I get everything before web and app, the biggest results can happen from this web to one of these app sequences. So start with the web and we get back all the items from event five and before, if I look for it the other direction, see what I can get. Only after I look for web, I match app and then starting there I get it and everything afterward.

This one is pretty similar to the one we looked at before. So web and app, I鈥檒l get back this hit and all the prior ones for only before and same web to app sequence will give me back event seven and this one only went to eight, so I get seven and eight back.

I think we have two more examples here.

Come on.

Well, if it does, the instead.

I鈥檒l hop back to it if it loads. But same idea here. In this case we鈥檙e going to see web and app. This is one of our early matches. So we get back 54321. And our results here. And then for the other way we would get back six through ten.

So it鈥檚 a lot of material that we went through. In today鈥檚 webinar, this is only looking at three of the topics we鈥檙e not talking about. Like I鈥檓 buying them. We鈥檙e not introducing exclude. There鈥檚 a whole bunch of other things that you can do that again, with practice, they become easy to use and incredibly powerful. So today we looked at the basics and then basics of within and after and how that can modify your results. And the only before and only after. So things that I would like to cover in subsequent webinars to go into more deep topics here, are going to be what happens when you use exclude in a sequence. What is the logic group, the container result that you get if you do things a certain way in the sequential, definition of a segment, do we ever need to use non repeat instances? And what is that even talking about. Let me just go and show this real quick. So if I open this. One.

So when I鈥檓 saying logic group here when you have checkpoints and you try to raise them to a higher level like person or visitor, you鈥檒l see logic group as the result here. Normally you would just see if I remove this normally you鈥檇 just see.

Normally you鈥檇 see visitor person. So that鈥檚 the logic group. And then the other thing that I鈥檓 talking about as non repeat instances is what this gear icon can do. So that鈥檚 one thing I鈥檇 just like to clarify in a follow up webinar as well. And then so one of the most useful things is saying only before and after with excludes and then lots more tips and examples, business cases and additional suggestions. So we will get to those hopefully in subsequent webinars.

If you come out of today鈥檚 session, just having a sense of how to start thinking about then within after, only before, only after, and the kinds of questions that they can help you answer, that would be the the ideal. This material is. It鈥檚 depends on your practice in your environment, with your data to, to get it internalized and really, enabled on it fully. So I demonstrated some of the ways that I鈥檇 recommend for everyone to go about validating, and testing here. Start with something small and known. If I look at my data set of 10 million visitors on a given day, and I try to apply some segments with their operations, it will work. It will do what it鈥檚 supposed to do, but it will be hard for me to be sure I know what it鈥檚 doing. So in my case, I searched through my demo data. I use some filters to say, just give me people that touch these three dimension definitions or excuse me, these three channels give me ones that, had no more than ten hits in the session. And then I just checked through a few of those to find, ones that would be useful for me.

The other thing you can do, too, is if you have a planned kind of test scenario, if I were to say, put some data into a dev environment or kind of tack on a fake clickthrough ID, and go to my site and take some actions, and then I can filter based on my campaign ID equals Andi powers. That鈥檚 another way that I can kind of give some very known data in for testing. The other thing too, is in analytics, you have these two nice dimensions that works exclusively by timestamp order. Well, mostly visit number and hit depth. So in my examples here I showed a method to kind of do a similar tactic in customer journey analytics. But if I鈥檓 in 蜜豆视频 Analytics traditional just web analytics focused, I have this hit depth dimension. It makes it really easy to see the sequence naturally open up this dimension, order it by its ordinal definition, and then I can do some tests. And I always like to use conditional formatting to just say all I care about is, did this happen or not? And that鈥檚 basically what I did here with the coloring as well.

Start small, validate it small, and then you can try combining them. So even when I was building these these examples for today鈥檚 session, I did not go in and build it all at once. I started with this filter. Then I looked at this one to identify smaller sessions, to study. Then I looked at people, and then I built these piecemeal. And if I was going to do something where I was doing one of the more advanced functions, I would still start like this. Verify that something works exactly as I intend, and then add it together.

And like I said, you can simplify some validation if you鈥檙e, just adjust some formatting if the numbers don鈥檛 really matter.

One other tip to it isn鈥檛 really applicable for what I went through today, but sometimes it can be helpful to see the way a segment definition is sent. An API calls. So I want to point out that if you鈥檙e turn on a packet sniffer and you monitor this validate endpoint every time you make a change within the interface to a segment or filter definition, you鈥檒l see your circles here update with a preview. That鈥檚 it. Sending this validate definition with the whole definition of that segment. So here I鈥檓 looking at I鈥檓 checking does a equal the value of page which is corresponding to this and so on. So you can use this to also see behind the scenes some more of how it works. And especially in more advanced cases that can be a helpful way to explore and try new things.

I have links in the Dec to both the analytics and the CDA sides of sequential segments or filters.

The content is similar, but they both give some different kinds of examples and sometimes use different, details. So I鈥檇 recommend that if you鈥檙e trying to study a given topic like within and after, check both of those resources. Because the engine is identical in the two platforms, it鈥檚 just pulling data from different, structures. One is webby analytics, one is from AP data. So both it can be useful.

And lastly, I don鈥檛 expect, we don鈥檛 have time for Q&A, but I do want to ensure everyone that we will look through the chat and Q&A and see what items were listed there that we might be able to cover and follow ups, and everyone will have access to the, materials from the deck, as well as the recording.

We鈥檝e posted a poll here. I would appreciate it very much if everyone could fill that out. Tell us, is this useful for helping you understand? Are you interested in seeing more like this? And with that, let me hand it back to you, Camila, for any final closing remarks. Thanks everyone.

Thank you Andy. Just remember, everyone to fill out the survey. We really appreciate you today for joining and listening to Andy speak about his topic. With that said, we can end the call. And we can call it a day. Anyone? Again, just fill out the survey, I鈥檒l give you a minute, and then I鈥檒l end the session. Again, thank you very much, everyone for attending. And thank you, Camila, for hosting.