Name: Stream. Sync. Scale: Activate Fullstory across your tech stack
Uploaded: 2025-10-16T18:20:40.208Z
Duration: 44 min 36 s
Description: Stream. Sync. Scale: Activate Fullstory across your tech stack

Transcript for "Stream. Sync. Scale: Activate Fullstory across your tech stack": Hello, everyone, and welcome. If you joined us for the previous session, welcome back. My name is Corey Kostick. I am a senior program manager alongside Dijanae on our digital customer success team, and I'll be the host for this session. If you're just joining us for Full Scoop Day, we are coming off of a presentation on one of our new offerings, StoryAI, which will help you find the why in your data. But now we're gonna talk more about how you can put that why to work. Before we dive into all of that, let's just go over a few logistical items. So we have the Fullstory on a regular basis, but this edition of Fullstory is extra special because for the first time ever, we are bringing you three deep dive sessions back to back to back, and we've organized this event in such a way that we're gonna talk through how Fullstory can help you from insights, beginning with insights, all the way through to impact. If you're not able to make every single session, that's okay. You're getting recordings in your inbox after the entire thing. Each of these sessions is packed with expert led demos and live q and a with our product teams. We're also hosting a fun swag giveaway at the end of each session, so make sure you stick around to get in on that. And just a friendly reminder, we have participant participation points happening throughout the entire day. So, the way to get into the running for the participation trophy is join as many sessions as you can, participate in the chat. GIFs GIFs do count towards that. And then finally, submit questions to q and a. Every interaction boosts your score, and we'll be reaching out to the grand prize winner via email after the event on how to claim your prize. Okay. Let's dive in. For this session on Fullstory Anywhere, we are joined by another all star panel of experts. So first up, we're gonna have Fullstory director of group product management, Chip Le. Welcome to Chip. He's waving. And we also have senior product manager, Jareal Acosta, here as well. Hey, everyone. Over the next thirty minutes or so, Chip and Jareal will show you how to activate your data across your entire tech stack. Now just as a reminder, we're gonna have a live q and a at the end of this, but throughout the entire session, make sure you're submitting your questions in that q and a tab as they come out. We will have moderators helping us with triaging these. So some of the QA will get a written response, and if any of them have, like, more nuance or complication to them, we will answer those live with Chip and Jareal. And if at any point during this session you're ready to see how Fullstory Anywhere can fit into your tech stack, make sure you're gonna click make sure you click on that explore custom trial button at the top, and that'll let your account team know that you're ready to connect for a custom demo. Okay. Without further ado, let's dive in. Chip, it's all you. Awesome. Thanks so much. Super excited to get to, share today about, some of the awesome stuff our teams have been working on. So let's let's dive in. Every single day, your customers are telling you a story on your website, on your app. It's a story they're telling through the clicks, their scrolls, those moments of hesitation, of of frustration. And for a long time, that rich story has been, you know, kind of tucked away or locked away, visible within your analytics platforms, visible within, you know, Fullstory's analytics platform. But what if you could unlock that story and put it to work everywhere as needed? And that's really the entire idea behind Fullstory anywhere. We make the most complete behavioral data, available and actionable across your entire, tech stack. So you can sync this data to your warehouse. It can help power better business intelligence. It can fuel your AI models, and your ML models that you're building. And where things I think get really interesting and exciting is where you can act on that data as it's happening in real time. And that's anywhere activation. It's by using these real time signals. It's kinda digital body language to create personalized experiences in the tools your teams are already using. And this need to act in the moment, it really leads us to kind of this critical point. Yeah. Let's see. Next slide. Appreciate it. The ideal customer journey is is really a live conversation. And I think this is really exciting as as product folks, as builders. But if you think about it, we've all been that customer where you're on a website and you're stuck. You're trying to, you know, get to some goal, you're trying to find what you need, and and you can't do that. Right? And so, like, what does that live conversation really look like today? Well, often it looks like a a chat window where you're looking for help, type out your problem, and you wait. It's not the best user experience. You know, by the time you actually get help, whether it's a bot or whether it's a human, you know, maybe you've already given up, you've moved on, you're having to repeat yourself, you're having to say what you were doing trying to get to that moment. But the thing is your customers are telling you they're struggling long before they ever open that chat window. They're telling you with every rage click, with every confused mouse movement, with every time they bounce back and forth between pages, that is their digital body language. And, you know, historically, the problem has been that our systems were just blind to this real time language. They couldn't hear those little whispers of frustration until they became a shout. And that gap, that moment of silent frustration is where customer loyalty is lost. It's, you know, I think, so strongly that, like, in this world where we can get instant answers from AI, where chat GPT and these systems are becoming like the way in which our, you know, our customers are engaging with with our experiences, with our brands, with our products, that gap is really becoming a chasm. So next slide. This is why we built Anywhere activation. We wanted to give your digital platform real time eyes and ears. Activation constantly listens for those crucial behavioral signals. There's like moments of friction or intent, and it doesn't just capture them. It helps you act on them instantly. It turns that moment of silent frustration into a proactive, contextually aware user experience, which is super exciting. Now, other tools have tried to solve this. Go to the next slide. There we go. But, you know, they ask you to really do the impossible, which is predict the future. Other systems make you manually tag every button, every field, every potential point of failure that you can think of. And if you don't guess it right, it you can't see it. And that's a broken model. We believe fundamentally that, you have to have the most, complete structured record of every digital visit to really make this work. That's what our patented full capture technology does. We capture every single automatically. There's no guesswork. And with activation, we can then stream those events in real time. They can turn any signal you choose in the instant trigger into those other systems. And then what's really cool is you combine those real time triggers with AI summaries. There's a real time context. They can feed that into your AI agents and the other systems to give the context and insights it needs to personalize those engagements. Alright. Next slide. The ability to act on that behavior in real time, is a game changer for your digital ecosystem. We're seeing customers do incredible things with this. Marketing personalization, streaming real time signals for predictive analytics and our models. But the area where we've seen the most immediate and dramatic impact is in customer support. We're seeing teams transform their CX operations from being, you know, these kind of reactive call centers into a proactive experience driven growth engine. And, it's just, you know, it's really exciting to see what's happening in the market. So go to next. So what does this actually look like? It means that instead of waiting for a support ticket, you can proactively offer to help a, you know, struggling user before they get frustrated. You can see, contact first contact resolution, rates, like, really skyrocket because your support agents, whether they're human or AI, can have the full context of what the user's session is right in front of them. No more of this, you know, can you tell me what you were doing? That person or that AI agent already knows what they were trying to do, and then proactively can assist. This means that you can reduce your support cost, but not by cutting corners, but by actually delivering a better, a faster, and, you know, a more empathetic experience. So I'm excited to show you all product demo to see what this actually looks like in practice, and then, I'll come back and pass this on to the next. So let's dive into the demo. Alright. Today, I'm gonna be demonstrating Anywhere activation. It's a new offering by Fullstory that unlocks several different new use cases and outcomes for your organization by really taking Fullstory behavioral data and putting it to work in real time. So we're gonna use this demo site here, Cargo Fashion, to give you a tour of several of those. So let's dive in and get shopping. Go to the store and see if we can find a product that we like. I don't know. The socks look pretty good, but I think I'm gonna go with the coat. Alright. So let's dive in. Let's add to cart. Okay. Let's add to cart. It looks like it's not working. I need some help here. Alright. So let's see if this AI agent can help me. I'll say, hey, hey, I can't add to cart. So what's happening here is Fullstory is taking this real time behavioral data, just everything that just happened. Tried to add to cart, it didn't work, I'm frustrated, and it uses AI to summarize my entire experience. Gives this AI agent real time lidar, the the ability to see what I just did. So let's see what it says. Says, okay. Let me review your sessions. I can see you're trying to add the denim dispatch code to your cart. Yep. That's right. I am. But you're getting stuck because you haven't selected a jacket size yet. I can see that. It looks like there's some issues here. There's maybe an error that I couldn't see. So let me go ahead and select the size and add that to my cart. Perfect. It looks like that, did the trick. So, this is huge for frontline support teams that are focused on AI agents because now instead of getting a generic error message, I can actually see something helpful. This can increase my agent resolution rates, can deflect more tickets. Alright. So this is just one way in which Fullstory behavioral data through activation is really coming to life. Let's take a peek under the hood, and we'll see what we've got over here in intercom. So in intercom, in my inbox, this is where I just was, asking this question. I asked Finn, you're like, hey. I can't add to cart. You can see under the hood, it's actually summarizing that session. So let's take a quick peek. Yep. There it is. That's all the details that Finn needed in order to help me get to a better outcome summarized in real time delivered to Fin via our integration. Well, let's say that issue gets escalated. Well, in Salesforce, we have a connector app that now has session summaries as well. So I can jump right in and see the summary and get to a resolution faster. That's decreased handle time, more savings, and happier customers. You can see we're building this out across the ecosystem. Here's a similar session summary that's right here within Zendesk. You can also take the same data, the same interactions, same behavioral triggers, and you can stream them out in real time. This is Segment. Segment is a CDP, but really you could imagine any of your CDP or Event Hubs. You can stream this type of, error message or trigger behavior. Marketers love this because it's so simple to create new campaign triggers and personalization without needing IT or engineering to create new events. So what might I do with that trigger? Well, let's take a look at Zapier. It can feed that trigger into something like Zapier where it can catch it, and then turn that into a session summary. So let's say this is a very high dollar cart, a VIP customer. We wanna make sure our concierge team proactively picks this up and supports this customer. So session summaries are super powerful because you can exact use case that you're trying to take advantage of. So let's say that my company is in France, and I want to make my summary in French, deliver that into Slack in near real time. Well, there we go. That's the same customer session we were just looking at. The same one we just were trying to shop. We were having issues adding the denim jacket. Now I'm getting a summary of that session delivered to my VIP team in near real time in French. Super, super powerful. So how does this actually work? Let's take a look under the hood. Here's our stream definition. In this case, we're streaming to Adobe. If we look at the definition, we can see that this is gonna trigger every time a user engages with a product that's more than $200 but fails to complete their order. And you can fully customize the fields and properties that are sent to that downstream destination. So what does that look like for analytics teams? Well, if we pop into Adobe, this is customer journey analytics. You can see here we're able to actually report on not only the events and clicks, but actually the individual element names all the way down to watching unique individual session replays. This allows you to connect these other product analytics ecosystems with Fullstory's data capture and stream this out for really rich, super simple to create without needing IT and engineering support to, to wire all this stuff up. So with Anywhere activation, there's really no limit to your creativity. To combine the power of real time streams, AI session summary API to create your own journey agents to consult customer issues faster, personalized marketing content, and create adaptive experiences based on users in the moment intent. Thank you so much. Alright. Hopefully, that was helpful and let you know a little bit more about how Anywhere activation works. Really excited, as product manager that helped kinda bring this to life with our team for you to try it, to dive in and and kick the tires. You know, try the APIs, try the streaming, connect it to your products, and, and then share your results. We'd love to learn about what you're doing with it. With that, I'm gonna pass it over, in just a second. Well, actually, let's see. We got one more slide here. I forgot this one. So, let's talk about results before I pass it over to Jeriel. So what we're seeing here is we've got, teams that are really, like, seeing tangible results. We've got a couple quotes here from some folks that have been using this to drive, to drive outcomes like decreased, handle time and, increased, you know, resolution rates. So it's exciting. We're excited to see what you're gonna do with it. And with that, I think I'll now pass the mic over to Jirael, and he'll talk a little bit about warehouse. Awesome. Thank you so much, Chip. And so what you all just saw were really powerful ways to act on real time data streams from Fullstory. And now let's say that you want something that really encompasses the breadth and depth of all your data from FullStory, that's where Anywhere Warehouse comes in. And so Anywhere Warehouse brings the full depth of FullStory's rich behavioral context to where you keep, all your disparate data sources. So it could be CRM, transactional, firmographic, and most importantly behavioral data in one unified view. And so this then gives you the ability to create a complete picture of all your customers. And so while Anywhere activation powers the in the moment workflows with specific streams of events and and patterns of events, Anywhere warehouse, really delivers the complete representation of Fullstory data to solve for things such as, fragmented customer profiles. And so it's very possible you're missing critical opportunities because the different views of your customers are in different data silos. And so your CRM tells you who, but not what they're doing in your digital product. And then your BI tool, does show you what is happening, but rarely shows you why. And so you might be missing insights into why trends exist. And so to understand that a bit more holistically, you'll want that granular behavioral context as that kind of connective tissue across your data sources. And another thing, Anywhere warehouse also solves for is is wasted time and resources. And so if you find yourself or your data teams find themselves spending significant time and resources on data preparation and troubleshooting, we've built anywhere warehouse to deliver your data with time to insight in mind. So you don't have to constantly, you know, switch between tabs and wrestle with a bunch of different data discrepancies, which can often take valuable resources away from what you would likely intend to do with anywhere warehouse, which is that very strategic and holistic analysis. And so to truly unlock the full potential of your behavioral data and overcome some of these common challenges, AnywhereWarehouse focuses on three powerful pillars, augment, create, and predict. These capabilities work together to transform your understanding of the customer and drive significant business impact. And so by leveraging these pillars, you gain unparalleled clarity into user behavior, enable proactive engagement, and fuel smarter data driven decisions across your entire organization. And so let's dive into each of these into a bit more detail. And so for first, I think for warehouse, lets you augment oh, actually, I can go back to the previous slide. Sorry about that. Thank you. And so first, warehouse lets you augment your existing data to create a truly complete user three sixty view. And so this means connecting behavioral data from Fullstory with your transactional data, customer profiles, or even support tickets, giving you a comprehensive understanding of every customer. This unified view, eliminates fragmented data, leading to a better understanding of the customer and their entire journey. Secondly, you can also create custom wide signals. And so you're probably familiar with rage clicks and other frustration signals within Fullstory, sort of out of the box. But you can go beyond standard standard metrics and define the specific sequences of granular behavioral data that matter most to your specific business. And so this allows you to create, you know, I think high level, but really precise points of view of where your users might struggle, or adapt in the digital experience. And so this deeper understanding that empowers you to, I think, much more proactively define and address new opportunities. And so, a pretty pointed example is you can track, let's call it, SKU substitution success rate, where you identify when users might watch an out of stock, elements on a page and then clicks on a related product leading to a purchase. And so you do gain that much, you know, added benefit of flexibility, with warehouse than you might get out of, just traditional Fullstory analytics. And then finally, you know, I think with this enriched data, you can predict future customer behavior. And so you can fuel your AI and machine learning models to forecast churn, identify, lifetime or rather high lifetime value, users, and personalized future investments with well labeled behavioral data from folks, Fullstory. And so this allows you to then, again, proactively engage and tailor experiences, ensuring you maximize, again, that customer lifetime value and that and drive targeted, business growth. Move on to the next slide, please. Thank you. And so I think to really drive the point home, I've seen and worked pretty directly with customers to leverage their anywhere warehouse data across different, teams and and really empower these different teams. But I think before jumping into the more function based use cases, a lot of it really stems from and starts with, what we kinda consider a foundational use case where, as I've been alluding to, you can really, leverage anywhere warehouse to create three sixty views of that customer life cycle, that, again, that I've been touching on. And so I've seen cases where, our customers have been able to augment what happened from existing analytics such as Google Analytics or Adobe, with the how and why from Fullstory, deep behavioral context to paint that, again, comprehensive picture. But, you know, from that foundational use cases, I've worked again with teams starting, for example, with marketing and inventory teams, that have leveraged warehouse to completely, complement their existing inventory and ecommerce platform data to do more accurate demand forecasting. And so by connecting what customers buy with, for example, custom definitions for what they've considered, teams have been able to more, accurately forecast in inventory. And so this moved them from beyond, you know, traditional purchase history to truly understanding intent and influence future demand. Secondly, I, plenty of times have seen sales teams use warehouse for, much enhanced lead scoring. And so by understanding which leads engage with high value features and integrating that data with, their CRM and invoicing systems, they've been able to better prioritize one to one outreach versus when to, for example, do scaled outreach that still feels personalized. And so warehouse does enable, that kind of use case as well. And then finally, and I think what what has been really exciting to see it and and work with several, you know, of our customers is working with data team, including ones here at at Fullstory as well, to use the warehouse data to empower AI agent training. And so as an example, I've worked with a handful of customers that have been able to build and train both agents, that their product team in turn use to quickly analyze customer experiences across behavioral, transactional, and firmographic data. And then also predictive models to predict, crucial outcomes like churn, and, again, finding out those, like, high LTV users. And so in many senses, this is actually just scratching the surface of how to unlock the value of anywhere warehouse. You know, I've seen countless other use cases across support, marketing, and this overall enhanced, BI. Next slide, please. Oh, there it is. Thank you. And now, you know, you're probably wondering how can you get this this data. And so Fullstory provides two paths to data driven insights. And so you can choose to receive raw event data directly into your cloud storage, for your own transformations or alternatively leverage full stories, pretty newly launched, but neatly organized and ready to analyze views sent directly to your data warehouse. And so on the left side there for cloud storage for that raw data, for those who prefer maximum flexibility, our raw events table provides the granular data that you might need sent directly to your cloud storage solutions, such as, Google Cloud Storage, Amazon, s three, or Azure Blob Storage, allowing your data teams to perform those custom transformations. And then, alternatively, for the ones that, are using data warehouses and wanna access that ready to analyze views format, our ready to analyze views are preprocessed and organized, making it incredibly easy to get started with immediate insights into your preferred data warehouse, whether it's Snowflake, Google BigQuery, or AWS Redshift with a few more to come. And so those are the ways that we can send data into these different platforms. And I think with that said, I think we have a demo to show you guys. Hey, everyone. Today, I'm really excited to show you around Fullstory warehouse. As I've mentioned, warehouse enables you to send all of your full story data into your warehouse to be combined with your other datasets for richer analysis. This data, piped on an hourly basis, is synced so that it's ready to fuel your KPI dashboards, recommendation engines, and even AI agents. So how about we start with one of these, your BI visualization platform? By combining with other datasets, we can provide pretty incredible insight into your customer's digital experience. This data can then be presented in any detail your leadership teams require to support their data driven decision making, whether it's business KPIs that focus on high level revenue and usage metrics for your c suite, or perhaps the lead gen organization wants to understand where to invest companies' marketing dollars and need a detailed view of attribution of how your different channels are performing. This is all possible because warehouse serves as the key data layer to give your teams those well informed insights. But how do we get this data in the right formats to the right teams in the first place? Because Fullstory understands every click, client side error, page load time, and, of course, anything you might have instrumented yourself, what you can easily find in Fullstory on research, as we see here, you can also find exported to your warehouse. And so we provide the raw data if you want total flexibility of analysis, but we also provide simplified views where we pretransform the data for you to provide ready to analyze tables to get to that same level of detail as you would expect using Omni Search in the full story platform and then some. And so looking at this, instance of BigQuery here on the left side panel, you can actually see how we separate and segment all the data that you would get via warehouse into dedicated tables. And so we have a dedicated clicks table, dedicated crashes table, dedicated events table, of course, and so on and so on. And so in these tables, and I'll use the clicks table as an example, we actually highlight the primary key and even include descriptions to ensure you know how these tables are linked to each other for your more complex analysis. And so to show you how ready these this data is, I'm gonna run a quick query using just this click table. And so let's say I'm just curious about which users have experienced the most rage clicks last month. What I can end up doing is I'll select for, say, user IDs. I wanna count of the rage clicks. I'm gonna take this all from the clicks table from this particular dataset here. And I wanna know this data for last month, And so I set my time range between September 1 and September 30. I'm gonna group this by the user ID. And then, again, I want this order in descending order by the number of, rage clicks. And so I'll click quickly run this analysis here. And very quickly, I'm now able to see the top five users last month who had experienced the most number of rage clicks. Now let's say I actually want to expand on this a little bit further. Maybe I wanna do a more complex, analysis or query. And so let's say instead of taking a look at the different user IDs, I'm actually curious about which are the top pages that experience rage clicks. But let's also say I'm more curious about rage clicks as well as net clicks, and maybe I wanna create a brand new metric called frustration signals that combines the the two. And so what I can do here is I can then run a analysis here that connects and joins across several different tables that I have segmented here to find me this exact, query. And so as you can see in this analysis here, I have counts of rage clicks. I have counts of dead clicks. I have the summation of both, and then I'm joining on the clicks table to find me the number of rage and dead clicks. I have source properties here to find me the page IDs where these clicks happened, and then I have page definitions to find me the exact page name as well. And so, once again, I did this for the month of September. And then in the query results here, I can very clearly see that the whole sign up page where I set the price range for this particular website takes experiencing a new, significant number of rage clicks. And then the promos as well is seeing a significant number of debt clicks. And so for me, this is a very clear, visible, way to tell which page I should be troubleshooting, on the back end and to fix up to optimize that that user experience. And so, hopefully, you're starting to get a sense of how much more flexible and robust your data becomes via Anywhere warehouse so you can run that much richer behavioral analysis. Thank you. K. And that is warehouse in a nutshell. And so really excited to see any kind of, further conversation, that stems, from this. But thank you for letting Chip and I, share a piece of of the Anywhere products with you guys. Incredible. Thanks, Chip and Jariel both. I personally have been working closely with the Anywhere team on other projects, and I think both of them had said this. But I feel like what we just saw is honestly just the tip of the iceberg in what in terms of what you can do with both Anywhere activation and Anywhere warehouse. Before we get to q and a, I just wanna clarify a question we've seen in chat around availability of these features, and these offerings. So for anywhere activation, which is what Chip presented on first in this session, it is a bit nuanced, but parts of that will be included with enterprise and advanced plans. And then anywhere warehouse, which is what Jerry will just presented on second, will be an add on. If you wanna get into the nitty gritty on both of those and the availability of them, you can go to the docs tab in the chat and then start with the help center article. It should lay out exactly what's available to you, versus what may be an add on. Okay. To that point, I'm sure everyone's already thinking about which of these tools they connect to first. So let's keep that momentum going. We're gonna move on to some live q and a. If you have more questions now as we're going through things, definitely drop them. If we don't cover them live, we will get to them after the fact. Okay. So the first one that we've seen quite a few times, I will try to share here. This one, I think, is for Chip. In the example with the chat agent, it was Intercom, I believe. Are there is there a list of compatible chat platforms, or is Intercom the only one? Yeah. It's a good question. So the way we built the session summary API is to be broadly compatible with any AI agent that can effectively call tools to hit other APIs to get data. So we've integrated with many other platforms, think like Sierra, ASAP, a handful of other ones, ADA. The the ability to do that is, something that we're gonna continue making more and more turnkey. I would say Intercom Fin is the one that is the simplest to kinda get up and running with, but just like a, you know, DIY kind of approach. But, yeah, if there's more that you're interested in getting support for, we'd love to hear from that and try to understand how to better support. Awesome. Okay. Next question. In the previous part of the presentation, Chip, you were demoing AEP CGA. I think it's just Chip. Anywhere warehouse would be a duplicate tech for companies that already have this. Right? Yeah. It's, it's a good question. So anywhere warehouse would be duplicate of tech. I don't think so. I think they're complimentary. You know, AEP and CJA is AEP ex Adobe Experience Platform is really kind of the data platform that underlies all of Adobe's products, including customer journey analytics. And so companies that are investing in Adobe ecosystem will already have some form of, data collection, in which they're using tag manager or some type of, you know, other third party tag management to to pull in data. But that often requires a lot of engineering lift to manage those data layers and to get that data, pulled in. So with Fullstory activation, you can now with low code, no code create new events, new event patterns, and stream that in the AEP without needing engineering to, go in and, like, actually tag or or, you know, write new code to capture data. So it really improves the, the feedback loop around getting new data and new types of triggers without needing new engineering support. I would say that warehouse similarly can provide, you know, a richer data set that you can pull into AEP for, other types of analysis that are really unique to Fullstory data set. So So I'd say they're very much complimentary, and that will continue to to be the case as we continue to invest more in adding more value into, our joint kind of Adobe Fullstory customers. Awesome. Next question is more centered around warehouse. If we have already been using data destinations, what does the transition look like into anywhere warehouse? Yeah. Absolutely. So you you can then coordinate, with, your teams your, customer success team to make sure that we can get you connected on the Anywhere, like, warehouse pipeline, essentially. And so as long as you have any of the destinations, either warehouses or cloud storage providers, we can then work with you to get that Fullstory data into that destination of choice of yours. Also, it's like a pretty seamless process. Awesome. Moron warehouse from Wallace. Do the elements seen in the data warehouse have the defined elements named as they appear in Fullstory, the app? Yes. Yes. So, one of the dedicated tables is the element definitions table, and then one of the columns in this table is the name as it would show up in, in in the Fullstory app. And so I'm sure it's already linked somewhere, but, I'll just post the documentation where we list out all of the tables and all the columns within, our migration to the to the warehouses for our ready to analyze views. But, yeah, there is a specific elements definition for that name there. Cool. Okay. Next one is from James. What are the expected costs for using Anywhere warehouse, specifically with Snowflake? James said they ran it for a month or so and found the costs were unexpectedly high. Not sure exactly why. Are there any upcoming plans for configuring the frequency of data updates and or methods to reduce compute time and costs? Okay. And and, James, I'd love to follow-up with you on this separately to get a sense of, like, what the what those costs were. But, we are actively, and have been working since launching already to analyze views, more publicly to improve, the cost efficiency for for our customers. And so we're in the middle of some of some work on that, right now, and so hoping to, you know, release this, pretty pretty soon. But, yeah, I mean, I think to answer the the question a bit as well in terms of what are the expected costs, it's the customer bears the cost of ingesting and then, the cost of the compute as you do the transformations. Ran it for about a month. Oh, I guess one other question too, James. I think this will depends on when you tried this anywhere warehouse connection because it's very possible before we launched our, ready to analyze views, which the intention there is because it's much easier to, work off of and transform. The compute cost would, would would decrease as well. But, yeah, I'd love to follow-up with you, afterwards to get a better sense of that as well. Cool. Yeah. It looks like he had another question in the chat. I I can't share it yet, but, he said he found that not all the data was there. So maybe this was before RTA, but he said, for example, in session replay, you can observe all the net network requests, but in warehouse, he was only able to see network errors or failed requests. Interesting. I can also follow-up with you on that as well. I don't recall if we are lacking other types of network requests outside of the the failed and errors request. I don't know if any but anybody on the Fullstory like Chad has a more direct answer for for that as well, but I can also look into that and follow-up with you on that. Awesome. Okay. That is everything I've seen so far, in terms of q and a. Let us do the last piece of this puzzle, this webinar, which is the top takeaway. Oops. Sorry. Top takeaway, and then let me just stop sharing that. Okay. So, if you don't know the rules, anybody, during the top takeaway segment, can answer the question in front of you, submit your response to the chat. And each of our presenters today, Chip and Jerio, will choose their favorite response for a total of two winners for some exclusive Fullstory swag. So the question is, how would you use Fullstory data to create a more personalized experience for your users, whether through what Chip presented on, Anywhere Activation, or what Jira presented on Anywhere warehouse. So go ahead and throw your answers in chat. And while everybody's submitting and and Chip and Jerry all are picking out what their favorite are, I will just remind everyone, if you're ready to talk more with us about Anywhere activation and or Anywhere warehouse, make sure you click on the explore custom trial button on the top right of the screen. That is the fastest way, to get a follow-up call scheduled with your team. Okay. What are we thinking, Chip? I'll I'll deter to you first for your favorite answer so far. Awesome. Yeah. I really like Fernand's answer. We've been talking actually working with several customers around this idea of streaming these session summaries into in store associates. So folks that are helping out, at point of sale, or, you know, helping kinda follow-up from a digital to a in store transaction. And the ability to have all that context about what that user was doing, leading kind of up to that moment is a really exciting use case. So it's it's really fun. That's got my vote. Amazing. Congrats, Ferdinand. Yep. You should be getting, contacted by us about your swag. And then, Jareal, favorite answer to how would you use Fullstory data to create a more personalized experience? Absolutely. I'll give it another second because I still see a few coming in, but let's see. Yep. Yeah. I mean, I think the the one that's, like, really, like, calling to me is actually, Jeremy Ramps about providing internal support reps with a complete view of what the customer is seeing. And so, you know, I think a lot of what Chip was saying in terms of activation, like, certainly, I I think enhances the the support experience. But one really interesting thing that you can do and see with with warehouse is that it can also complement, I think, the the support use cases. And so, when, you know, you have that real time activation, that real time, you know, use case, you can also pull a lot of that historical, context for that particular, say, customer, and I think really create a really bespoke, response to that particular experience, not just rather to that user, not just based off of the the sole experience that you're trying to, let's say, triage or or to to to, remediate, but to also kinda pull in some of, like, their more historical context that provides, you know, I think a really rich and deep understanding of the customer or or the end user being seen, you know, by by your company. And so that's what I think an example where you can actually leverage both warehouse and activation for a much richer, user experience and user interaction, with your with your end customers. Amazing. Congrats to Jeremy. Yeah. As you saw from Dijanae in the chat, we'll be in touch via email and deep with details on how to claim your swag. We are getting close to end of time here, so I just wanna say thank you to Chip and Jariel, and thanks to everyone for all the engagement. We had a ton of questions in this one. I know it's a little bit more technical, so really appreciate the engagement. If you haven't already done so, please be sure to fill out the survey that should be popping up for you shortly here. And if you're sticking around for all full scoop day, which you definitely should, we have one more session to go. Join us back here at 3PM eastern, 12PM Pacific for our final deep dive on Fullstory workforce hosted by my wonderful peer, Christy Quinones. That is everything for me and Chip and Jariel. Class is dismissed, and we'll see you in the next session. Take care, mate. Bye, guys.