[music] [Eric Matisoff] Hello, everybody. Welcome to Adobe Analytics Rockstar.

Everyone, get your T-shirt, get relaxed, get cozy. We've got ten amazing tips for you. My name is Eric Matisoff. It's great to be here. I've got a fantastic of slew of Adobe Analytics Rockstars with me tonight and I don't have a jacket on, so no one's going to tell me to take it off.

We have a really, really fun session. It's like one of my favorite things at Summit every single year, and I love that we always get great excitement around it. We have great tips, we have great people, we have great everything. And so should we get started? Let's get started. Okay, so before we get started, I'll tell you what it is that we're actually doing here. So the whole event is a competition. We have five lovely analysts up on stage with me and they are competing for prizes. Now, everybody gets a prize because they're working hard up here, but the prizeiest prize goes to the winner. And you all decide who deserves that prizeiest prize. They only have two tips, eight minutes. So we're going to be going fast. You're welcome to take pictures of the slides, but in a couple weeks, all the slides will be available on the Summit website, too. So it's totally up to you. If you don't feel like taking out your iPad Pro and taking photos, it's perfectly OK. All right, so let's meet our Rockstars, shall we? Yeah. All right.

The stars of the show.

All right, so our first rockstar is Jennifer Dungan from Torstar. Give it up for Jennifer.

Jennifer is the reigning champion of the Adobe Analytics Rockstar program from last year.

So she's going to set the bar high because she's got to go first. All right? So take those notes. Get ready. Next up, we have the one and only Jimalytics, Jim Gordon.

He's very humble, apparently, but he works at Blue Acorn iCi and does so much more in our hearts and in our minds.

Next up, we have Mandy George. Give it up for Mandy.

So Mandy works over at Best Buy Canada and is the best Canadian, is one of the top two Canadians on stage with us today.

Next up, we have Matt Bentley. Give it up for Matt.

Matt flew across an ocean to be here with y'all. He's based in the UK, works for Loop Horizon, and he's got some amazing tips for you, too, so, you know, just get excited for those. And last but absolutely not least, we have Katie Klein.

Katie has delivered many an amazing presentation. Always looking so fit. She works at Macy's, so it kind of makes sense, and it's great to have her. Should we get started? What do you think? Yeah.

So this is actually the order that we're going to go. We've got them organized nice and easily, and so we'll kick off round one with Jennifer. Should we rock and roll? Yeah, let's do it. Flicker. [Jennifer Dungan] All right. Are we on? Perfect. All right, so, for my first tip, I'm going to be talking about enhanced lists. So, we all know there are many times where we need to track a lot of data in a single combined element, but our out-of-the-box solutions are limited to lisped and list supported props. As we know, there are challenges with these. Lists or props that are supporting lists, you can have as many as you want, but they're limited to 100 characters in total, so your risk of data truncation is really high. And, of course, multiple repeated values are de-duplicated. On the other hand, lists, you can pass as much as you want, but you only have three of them, and repeated values are still de-duplicated. So we have a couple of considerations. We have a simple solution which is going to rely on no repeated values and no need to correlate events or additional dimensions to your list. And of course, the opposite of that would be the advanced solution, which of course, as you guessed it, is the opposite, repeated values or correlated values. So, for our first simple solution, we're actually going to use a single list dimension and using prefixes and classifications to turn it into many. So I'm going to use a prefix of tag for my asset tags, a prefix of cat for my categories, a prefix of auth for my authors. And you will notice I am passing multiple values here. And cust for my custom properties. So I'm going to make a list dimension and I'm going to set up my classifications for each value that I need. Pay attention to your delimiter. It's going to process everything into separate items based on this before it gets to your classification rules. And now when I build up my classifications, I'm going to use a simple RegEx using groups so that I can pass just the value into my classification. And this will become more clear when you see the result of the test. My tags are going into my tags classification, my authors are going into the authors, etcetera.

So you can still pull out your data as your list, as all of the raw data, but you can actually pull out your classification as its own unique list item, clean data, no prefixes and you can even correlate those related assets together into one table, such as your authors. Now, our advanced solution, we're going to use our product list. Now, I know what you're thinking. You can absolutely use this with your shopping cart data, but you might have to use a little bit of tweaks to change up how you're pulling up the data. We are going to use the product category, similar to our prefixes, to denote the use, our product name to identify our items. We're going to ignore our revenue and quantity because we don't need those. And then we're going to correlate this with merchandising eVars and numerical events.

So here's an example. I'm tracking a marketing asset such as a wall. I only have one of these, so I'm only going to pass one into my event. But you can see that I'm passing additional eVars. Or in a simpler solution, I have multiple newsletter promos. I don't care the distinction between them. So I'm actually going to pass a numerical two in for each repeated instance. That's your repeats. And now when I pull out my report, all's I have to do is pull in the category first to keep it isolated from every other product data, and then I can pull in, oops, sorry, my eVars or my products or any combination of them. I'm using my numerical events as my metrics, and I can even create calculated metrics from those. So, in summary, our current list items are limited. So our solution is to use some creative solutions, such as products and classifications, to make multiple. And finally, our results are now limitless. Thank you.

Awesome.

Thank you, Jennifer. Pretty killer tip right there. You know what I really love? I'm a big fan of hacking what's possible, and that's especially nice. I will say in Customer Journey Analytics, you have unlimited list variables. This is Adobe Analytics Rockstar. So I appreciate the information. Next up, we have the one and only Jimalytics. Jim, take it away.

[Jim Gordon] Hey, y'all. Little horse.

Oh, there we go. My tip's called launch into CJA. Being a human is hard. Doing laundry is hard. It just never stops. It just never ends. Paying bills is hard. That's a kid's birthday party. Kid's birthday parties are really expensive if you haven't paid for them yet.

Using Adobe Launch's global search is hard.

What if I could tell you it doesn't have to be that hard? Now, we don't have as many rule sandwiches with Customer Journey Analytics, to quote Jan Coons over there, so what we can do with Web SDK and with CJA is actually export our launch implementation and then import it into AEP/CJA. So that means we can fix errors faster, eliminate unused rules and spot-check business rules. So to do that, what I'm going to do is first create a data element based on the rule ID. And this is a long GIF or GIF or however you say it. So I don't want to waste my time. And then you integrate it into the rule action, which is Web SDK update variable, pass it into your, or actually you could probably do with the global XDM object. I'm going to get a little too nerdy with you all here.

And then what you do is you export your implementation and all the resources are actually on the bottom right-hand corner, jimalytics.com/rockstar. So all this stuff that you're seeing, you can download it and do it yourself. But what I'm doing is I'm consolidating all of my rules so my actions are combined when you have multiple actions in a single rule using, using, so you can see the role names actually ends up in CJA. But I'm using Tagtician with Excel and I'm massaging that data into Excel and then sending that straight into AEP. So now I can see my XDM payload, my rule conditions, my rule events and rule order without having to enter Adobe Launch. And the outcome is right here. It's a little cody, but you can actually see what it looks like when you put all of your actions and stuff like that in there. So you don't have to leave the interface and you know exactly where to diagnose issues when you see issues. So you don't have to use that global search. That's what I got. So being a human just got a little bit easier. All right, yeah.

Very cool. Thank you, Jim. I love any opportunity to connect Adobe Launch Web SDK to CJA and beyond. Very, very nice. - All the abbreviations. - All of the abbreviations. We've got more than that. So next up we have Mandy. Come on up and share your tip. Give it up for Mandy. [Mandy George] Thank you.

All right, so my first tip today is creating in-report normalized metrics for anomaly detection. So when you're looking at the performance of different features on your site over time, initially you might think that looking at the spikes and drops in traffic and usage over time is going to tell you a good story. So if we look at the example of search metrics here, we might think that November and December had the best performance last year and May had the worst performance. But this doesn't take into account other factors that impact our sites, such as seasonal changes. So what we want to do is actually create a normalized metric, that way it takes into account these other factors that are impacting the entire site. So what we're going to do is we're going to normalize our searches against visits, and you can use pretty much any metric for normalization, custom links, occurrences, visits. In this case, we have searches on our left and visits on the right, and we're going to select both of those, right click and create metric from selection and then hit divide. When you do this, the metric that's on the left is going to be the numerator and the one on the right is going to be the denominator. So it makes a difference to what order you put them in in the table. Once you divide, this creates a normalized or, sorry, yeah, it creates a normalized metric that comes in as a whole number. Now this metric is an in-report-only metric, so it's not going to show up in your components list on the left, it's only going to be available within this dashboard. So if you have a lot of metrics that you're trying to analyze, by building them directly in the table and keeping them out of your components list is going to keep that list clean so you're not cluttering that list, especially if these are only going to be used in the current report. So when you make this, it comes in as a whole number by default. So what you're going to want to do is click the info icon and then click on the pencil icon to edit it. Once you're in the editor, you can change it to a percentage and then change your decimal places as well. I prefer two decimal places. You can use whatever granularity works for you. You also have the option of changing the name of the metric. I personally leave it how it is because the name includes both of the metrics that created it. Once you've done that, you hit save and now you can see the metric is a nice percentage instead of just a whole number. So this takes into account our overall increases and decreases for our entire site. But if we want to make this a little bit more visible, we can use the setting options and change the background formatting to conditional formatting. And with this, now we have some colors to show us which months perform the best very quickly. So here we can see that November and December, despite initially looking great, actually only have average performance and May actually had the best search performance overall. But we can take this one step further by creating an alert to let us know automatically when spikes or drops happen. So again, we're going to right click on the metric here and select create alert from selection. And this is going to take us into the alert builder. And here we're going to pair it with anomaly exists and the 95 or 99% thresholds. And using this, it gives you a quick way to see when your metric either spikes or drops and takes into account overall factors that impact your entire site, not just the specific metric. So now we've got an easy way to see when and where changes are happening, how our site is performing, and we've done it in a way without cluttering our components list.

Awesome.

Thank you, Mandy. I always love a good new calculated metric myself. I don't know about you all. So next up we have Matt, who is going to present his first tip. Ready to rock? - [Matt Bentley] Yeah. - All right. Give it up for Matt.

So I'm going to talk about the hidden segment, and what I mean by that is that segments in Adobe Analytics only have three levels of granularity. You've got visitor, visit and hit, but the data has four levels of granularity because within each hit, you can have multiple products. And what that means is that if you get asked to segment on product-level data, you're always going to end up with misattribution. So the example I'm going to give is I'm buying a laptop, and with that laptop, I'm going to buy antivirus software. And with the antivirus software, I'm going to buy a gaming pass as well. So I've got computing hardware, computing software, and gaming as my categorizations. And you can see that being sent into Web SDK. Key thing is they're all in the same hit.

And I like this. This is a thing of beauty. You can see that they've all been attributed nicely, my products down the left-hand side and my categories across the top. But my stakeholder says, Matt, I want to look at computing versus gaming. So I want you to group up computing for me so I can look at computing versus gaming. And so I do the obvious thing. I go and build a hit-level segment, and you can already see where this is going. Those three products were sent on the same hit. Oh, no.

It's attributed gaming under computing. So why is this laptop categorized as lawn equipment? Why is this blender soft furnishings? And what you're going to end up with is a situation where your stakeholders don't trust your data. They start to make decisions that aren't based on data, you're going to lose revenue. Then probably it's the end of the world and everybody dies.

So what is a segment? It's just a way of classifying something. And within Adobe Analytics, we always had classifications. Some of the other tips were using classifications, and classifications are on the dimension level, so they will inherit the granularity of the dimension that you classify. So I'm going to create my classification, then I'm going to go into classification rule builder and other classification types are available, so you can do a segment upload if you want. I'm going to go in and I'm going to do my little funky RegEx and I'm going to call it primary group. And what I'm doing, basically, is I'm splitting on the underscore that I got in that categorization variable, and I'm going to assign the first value that I get to the category that, sorry, the classification variable, and I'm going to assign the first value that I get to that classification. And there you go. At the end of that, you've got this correct attribution now. So I've managed to group up computing, and now my stakeholder can look at computing versus gaming and you get correct attribution, your stakeholder trusts your data, and the world is saved for another day. Thank you.

Thank you, Matt.

That's good stuff. How we feeling? Getting whiplash? I see people recording, taking photos. It's good. There's lots of good tips here. Four for four. I'd say 1, 2, 3, 4. Now we're ready for Katie. Come on up.

[Katie Klein] Make sure I know how this works. OK. Hi. So this is unique data view attribution. So one report suite. Nope, just kidding. Data view to rule them all. So in CJA. Thank you. It goes further than that, so. So in CJA, you have data views which are similar to report suites, but for the purpose of this, we'll keep it simple. And last day of Summit, hopefully, you've heard a lot about how CJA is really cool. And one of the great things is that it goes beyond behavioral data to be able to put all sorts of data sources in there, depending on what you want to do. And you're probably thinking to yourself like, yeah, this is great. I'm in analysis workspace. I've got a bunch of stakeholders. Whatever I'm doing is streamlined and easy, and that is not always the case, because what happens when there are more questions than answers? Uh oh. Analysis paralysis. So I was going to make an attribution joke here, but I don't want to take all the credit. No? Oh, sorry.

Thank you. So how do we break out of analysis paralysis and get back to the happy place curation? So the cool thing about data views in CJA is that you can actually do component-level attribution. So for anybody who's not as familiar, data views are going to house your components and that's going to be similar to metrics and dimensions. And so what you can actually do is go into the interface. If you're an admin or somebody who does configuration, I would also say that if you're not an admin or somebody who does configuration, talk to your admin or the person who does configuration, maybe your ops team, and say, hey, this looks really cool and it's going to make my life easier. So we should probably do this. So on the right side, you'll see attribution models that you can choose from and there's a whole list and you can take your component, map it to that and then also think about the lookback window. So that's the amount of time that you would associate something that you consider a conversion to the events within that timeframe. And when you do that, you have the end result where you actually see orders that have last touch and 30 days. And the reason that this is neat is when you have a bunch of people who are doing different things with different use cases, you think, ok, great, we have one place that everybody can look at this. But when you have different groups who are actually looking at different types of attribution, it's not always the easiest way for you to have them be enabled on the solution by looking at everything in the same place. So by putting them in a really familiar space where they can look at things aligned with how they typically do it using analysis workspace, it actually gives them a bit more comfort. And we get back to a happy place with curated data and happy analysts. Love it.

Thank you, Katie. Amazing. Round one is complete. We are going rapid-fire here and I love it. So how are we doing on timing? All right, Jen knows exactly how much time she has. She's ready to rock and roll. Okay, so Jennifer, we're going to go with you then we're going to go right down the line. Go again. Stick around. We've got voting, we've got prizes, we've all sorts of fun stuff, okay? Jennifer, take it away. We're going to mosh pit after this. We're going to mosh pit. All right. As long as Shaq is in there. Yes. Thank God. All right, for my second tip, I am going to be talking about a trended fallout report. So this actually came about because someone in my marketing team was going in every week to my fallout report, copying the data into Excel so that they could see the trend over time. Yeah, this has got to be simpler than that. So you're all used to this. A fallout report is an aggregated view of the data for whatever date range you're using. So what we're going to do is we're going to create segments from each fallout step. And you can do this simply by right-clicking and create segment, or you can even create your own. It's a simple sequential segment, nothing fancy. And then you're going to actually build this out into a table. So as you can see, now, here's a little bonus tip. I'm showing you a side-by-side table of my new segments versus not. I see this question come up all the time. Why doesn't my table match my fallout? Your fallout relies on sequential sequences. So if you don't have sequences, it's not going to match. So you can see my data with the sequence matches my fallout.

Now we're going to actually do the calculations. So I'm going to put in my calculations at each step. I recommend keeping only success or fallout, whichever is important to you in the table, just to keep it clean. And you will notice that the success rate is actually based off of the first step, whereas the fallout is actually based on the previous step. And the math for this is really easy. We just take either the current step divided by the first step or the current step divided by the previous. And for a fallout, it's just the inverse. So you just have to subtract that value from one. Now that you know the math, you can actually go a little bit further and customize it to yourself. We like to have our success rate based on step-by-step, but then I'll add a last column which shows me my first to my last step. And unlike your fallout reports, I can start at any step in this journey. I don't have to start at my all visitors, but I can keep it there as a reference for my team. And the best part, I can break this down by any date granularity that I'd like. And I can graph that. I can also graph all my success rates against one another to see how they compare against. So in summary, fallout reports, while good, are all based on aggregate data. So our solution is to build out our own custom solution with segments that match our fallout steps. And we're going to break down by our time dimension. And now our users can see at a glance how the trend of critical flows are going at a glance without a lot of extra effort. Thank you.

Amazing. Love it. Jennifer, super cool stuff. I love taking fallouts and messing with them and adding segments and calculated metrics. It's the best. All right, Jim, you ready? You got five minutes and 28 seconds. Let's see how fast you can talk. Pretty quickly.

All right, tip number two. Not the analytics supervillains conference. Just kidding. It is the analytics supervillain conference. And I want to thank all of you for providing good top cover in the analytics rockstar session because our mission statement, just to remind all of you, you should have it memorized, we exist to fabricate digital data and make lots of money, driven by powerful robots that elevate our presence while minimizing our detectable footprint. We all know this. I didn't ask you to rehearse it this time. So thank you for putting up with me saying it again. In other words, we love committing fraud and it's so easy to do. Let's talk through our 2023 performance. We are up 15% year-over-year net revenue. We have added over 180 million headcount thanks to ChatGPT, and our fraudulent site traffic is up to about 20%, which is fantastic. Let's go through our 2023 Fraud Awards this year. The first one is the Pitch Torpedo Award. We don't like people buying anti-fraud tools. So what works? Your CDN does bot detection with a runner up of what does a bot even want to do with my website? Lifetime Achievement Award goes to none other than, I know you know who this is, Sharky, the Placebo Bot, from 160.0.0.0. Sharky is such a mainstay because once you block Sharky, oh, you've solved for bots. Finally, Breakout Technology Award goes to Google Analytics 4. So let's talk through.

Let's talk through the threat on the horizon. And I think you know where I'm going with this. It's AEP. So previously, people couldn't do anything about us because they had to work together, right? I mean, you collect the data in the bot detection tool and then you're like, okay, I guess I gotta put it somewhere else, and then somehow I have to work with my IT and BI Data engineering. Anyway, it all falls apart. It's hilarious. With AEP, unfortunately, we're in big trouble, right? Because you don't have to send it to all these different places.

And how they do it is really simple, and it's insulting, because you add the ECID and you send it to your tool of choice, whether that's check AI, FouAnalytics, or some other tool.

They classify the data, right? Because they look at the behavior and they run analysis on it. And then what they're doing is they're sending it right back into AEP, because AEP apparently eats whatever data you give it.

This is an evil slide thing, too. Made sure we covered all of our bases here. Computer's over here. - Point it that way. - Oh, point it that way. Yep. I was trained in an evil course where everything was backwards. So now people can easily segment in CJA bots versus humans.

You can easily filter out bots in. They can easily filter out bots in their data views. They can exclude the bots in their data streams, and then, even further, now that we can measure humans versus bots, or they can. I'm not doing this.

They can remove us from marketing by isolating humans in Real-Time CDP saving up to 20% of their marketing dollars, which is horrendous because we are dependent on those marketing dollars to make our money. We're not going to hit our 2024 benchmarks, and they can exclude us from all the cool stuff that they're doing on their website. And I love the cool stuff. And that's like with AJO. They can keep using these audiences. So people want to make better decisions, they want to save money, and they want to market more effectively. So we've got to come up with a new stick. Villains are losing. All right, anyway, back to our regularly scheduled programming. Go to supervillaincon.com. It's highly secure. And check out our latest stuff. I will see you all next year.

Thanks.

All right, back to Adobe Summit and away from our supervillains. Thank you, Jim. Next up, we have Mandy. Tip number two. You ready to rock and roll? All right.

All right, so the second tip that I have for you today is how to make visit-in-hit segments. So when we have stakeholders that ask us about questions on different sections of our site, a lot of times we'll build out a segment that has a higher level and then containers at a lower level. So hit in visits. But you can actually do this the opposite way, and it can be quite useful. So say we have a stakeholder that comes to us, and they want to look at the performance of video game consoles on our site, but they only want to look at the customers that have also seen video game accessories and software. So our first instinct would be to make a regular visit-level segment. But the way that our segments work is if it matches the criteria, it's going to pull in all the information. So it's going to look at more than just video game consoles and we're going to end up with a table like this with a lot of product views for categories that we don't care about in this analysis. So what we can do instead is we can make a visit in hit segment. So the way we're going to do this is we're going to start off with our typical visit level segment with all three of our criteria using the and operator. And then we're going to take that segment and we're going to put it into a hit-level segment. So the visit level becomes a container within it and we duplicate our variable of interest in the hit level part. So now we have a hit that's looking at video game consoles but only if it's also associated with a visit where they've seen all three of our categories. So once we do this, it's now going to return a table that looks, sorry, instead of returning a table that looks like this with categories that we don't care about, it's going to return a table like this. So every hit that's returned has video game consoles in it. You can see this because the row total matches the total for the table as well. There are still other categories but those only show up in the same hit as video game consoles. So now we have a much cleaner table to report on to our stakeholders. But we can take this a step further and do this with sequential segments. Say we have a stakeholder that's interested in how people who see our homepage are performing, but only if they also do a search, see a product and add to cart. So we're going to do the same type of analysis here but with a sequential segment. So we have our page view equals home and then our search, our department exists and our cart edition exists. So we have all of these conditions in a visit segment and then we're going to put that into a container with a hit level around it and we're going to duplicate the homepage in that hit-level part of the segment. So that way everything that gets returned has the homepage view associated with it. So if we were to just go with our regular sequential segment, we would be giving our stakeholder a table like this where we can see the page views each month, but we have to break it down by the individual pages to understand whether or not they're actually seeing the homepage. On the other hand, if we put the hit level segment around it, we can now see that all of the page views are coming from the homepage. So this is giving us a much cleaner table that prevents us from having to use additional dimensions to break down the information. So this makes a cleaner view for our stakeholders, that way they have an easier table to read and we have less dimensions that we need to bring into our analysis. Thank you.

Thank you.

Awesome, Mandy. I love it. Like, I'm still trying to wrap my brain about visits inside of hits. It's great. Very, very cool stuff. All right, Matt, you ready? - Yep. - All right. Give it up for Matt.

So who uses counter eVars? It's an audience participation part, one.

More than I was expecting, but the apparently secret joy of counter eVars, and they've been around forever. This isn't like a fresh tip. I mean, I found articles from 2008, but I'm going to tell you about them anyway. So it's a dimension, it works like any other eVar, but it will persist a number, and then you just increment that number, so it will persist the value. And then when the thing happens that you're interested in, you can add one to it or you can add more than one. You could add five if you like. You can take numbers away as well. So if something bad happens that you don't like, you can take numbers away as well. I just repeated myself, but I am very nervous. So this is how I set it up. This is how I set it up in my data layer. So I'm going to count articles. I'm going to be interested in the number of articles that people use when key events take place. And you set it up like any other eVar, but you just select the type of counter and then you can set your expiration so it will persist like any other eVar. So I'm setting this one to persist at the visitor level. Because it's quite granular, I'm going to group them up into cohorts. So just to make it a bit easier to read. And then this is the, well, the cohorts one is the one at the bottom. This is the kind of table that you get.

You're going to use it with key events. So the key thing to understand about your counter eVars is that they're not the end goal in themselves. You're going to use it with your KPIs and it's going to add context to those KPIs and you can set different expirations. And again, understanding those expirations is important. So if you set for visit level, for example, then it's going to reset itself to zero after every visit. And then just watch out for some gotchas. So it will overcount if you use it with visits and visitors. Because for example, if I view one article and then I view another one, I'm going to be in the one and two cohorts. So you will get over counting. But that's kind of the point because if you use it in conjunction with conversion metrics, for example, you want it to overcount because you want it to allocate the denominator to all of the cohorts would only allocate the success event, the conversion to the cohort that should receive it. And so you get the correct conversion rates. And then the other gotcha is just safari ITP because it's, you know, it's based on cookies. And so if the cookie gets deleted after 24 hours, your counter eVar is going to reset.

But so what? Like, that's really nice, but like why should anyone care? So I'm just going to give a few examples and wow, I've got loads of time. I'm going to give a few examples. So the articles example that I gave, let's say that one of my clients is spending $300 an article to generate content. What if they find that when they combine the counter eVars for articles, there's no correlation between article generation and conversion? They're wasting their money. They can stop doing that. How many products were viewed before a purchase? You can feed that into your A/B testing program. If lots of products are viewed before purchase, you could say, well, maybe people are researching before they're converting. So why not I add trust indicators? Why don't I add more product information? Why don't I make the product information more prevalent and see if that drives conversion? And then another good use case might be KPI contributions. So again, thinking about supercharging your A/B testing program, let's identify some micro conversions around the site and find out how they actually correlate to the real KPI. We're going to get a lot more volume in those micro conversions and so we can use those in our A/B tests instead of the main conversion event. You're going to achieve significance faster and you supercharge your A/B testing program. So that's just a few use cases for counter eVars because I think they're pretty cool. And I think we had about five hands for people who are using them. So hopefully you'll go back and you'll start using them a bit more. - Cheers. - Awesome.

Thank you, Matt. Love it. Those sneaky counter eVars, I love hearing an opportunity to use them. I don't know, whenever you hear a random idea of like, oh, here's the counter eVar use case. It makes me smile. So we have our final tip to share, and then we're going to get into the nitty-gritty. Katie, you ready to rock it? All right.

So I have, like, five minutes, which is a lifetime. How's everyone doing? I talked really fast, so hopefully you picked up at least on the puns, even if you didn't pick up on the tips, so. All right. AJO reporting in CJA. So the pun that I missed in the first one is that with great omni-channel analytics comes great responsibility.

Fewer boos on that one. Okay, I get it. All right. CJA is super flexible. We get that. And that was a more enterprise use case, right? You're thinking about just, like, general data in CJA. Let me not smack these together again because that was probably kind of loud. And for this one, if you're not using AJO, maybe it's a little more of a niche use case. But I do think you can think about it from a journey perspective, right? So anytime someone is interacting with some sort of collateral or channel or tactic for your brand or your client, depending on who's here, you want to be able to see that clearly in the context of the business, right? You want to think about business terms, you want to think about how that fits into what you want to do for your analysis. And so what we want to do is think about what touchpoints look like. So if you're not in AJO or Journey Optimizer, this is a journey canvas, looks super cool, is not existing in the same way in CJA. So how do we make it feel digestible and actually give cool visualizations that CJA does for something that's totally separate? So if you look at each touchpoint or node, it has a naming convention. So, marketing channels, most of you have probably seen the UTM parameters. You think about all the word salad gobbledygook and you try to make sense of it, right? And everybody knows that CJA is super cool because you can use derived fields. So what we want to do is look at the node names without derived fields and say, yeah, super functional. Like, maybe not as pretty as when you use it with, ooh, shiny node names with derived fields. And the difference there is you're going to take all of your little attributes, and you're going to distill it down into something that feels more tangible and actually gives you a sense of what those journey touchpoints or what those nodes actually mean.

And when you think about AJO reporting in CJA, even though there's reporting in AJO, you want to be able to map all of these different touch points back to your actual, broader data sets. We looked at how you can have lots of different source data in CJA. And so if you think about it in the context of CJA and how AJO translates into that, you want to be able to think about that alongside your behavioral data or alongside your CRM data or whatever other data sets you have in CJA. So every great journey needs data. But we also want to make sure that we're understanding what that data is. We want to make sure that your data nomenclature is clean. But then you also want to think about how you're distilling that down so that it's actually tangible and digestible for all the different awesome users that you're going to have looking at your individual precious stitched dashboard in CJA. That's it. All right.

Thank you, Katie. Thank you, all five of you. Amazing job.

You know, I wanted to point out that I did mention Katie's amazing outfit, but I didn't mention the coolest sneakers on stage. Look at these. Right? I don't know where to buy those, but I'm on my way. All right, so it is time to vote. So get your phones out. Get your, yeah, just your phones. You don't need anything else. But first, let's talk about prizes. We didn't even talk about prizes. So here's my question. Get loud. Clap if you want to hear more content shared from these five people.

Me, too. So I want to make sure they're enabled to do that. So the first thing that everyone is getting is a super high-quality podcast video cast setup. So they're getting a really nice, high-quality microphone, a really nice, high-quality amplifier thingamajig.

That's the technical term, worth over $500.

And if you already have it, you get a gift receipt. Don't worry. And then the prizeiest prize goes to the winniest winner, who gets an additional $500 as well. This is powered by-- There's $500 that was generated by Adobe Firefly, so.

Okay, who's ready to vote? Got your phones out. Get your cameras on. We got a QR code on the next page. We're gonna leave it up for a couple minutes and then we're gonna see who the winner is, okay? I've also got a list of everybody's name, what their tips were, in case you forget. Who's ready? Ready? Let's do it.

This is fun.

Yeah. Jim's going to sing the jeopardy song.

All right. We have just barely a winner. It is very close.

So the winner is the one and only Mandy George. Congrats. Mandy.

Come on up.

- Congrats! - Thank you! Oh my god! It's a hair.

Awesome.

All right, so, Mandy and everybody, congratulations, you all rocked it.

We also have one last fun thing to treat you all too. So at the back of the room, we've got 50 Adobe Analytics for Dummies books. 50, I think, A is for Analytics books with Hila Dahan. We're going to be giving them away for free, signing them, having fun, and then we can all head home and finally get out of Vegas. So thank you all. Happy Summit.