Luxury Retail: Using Wealth & Behavioral Data to Win Without Third-Party Cookies

This webinar is specifically for luxury marketers, and we're gonna be covering using wealth and behavioral data to win without third party cookies. So very, topical subject here, something we hear a lot from our clients. And, again, looking forward to presenting on this on this subject matter. Quick introductions here. My name is Steve Similartes. I'm a senior account executive at Windfall. I've been with Windfall for almost four years now. So specifically helping retail brands get a better sense of how they can use our data and action it within a lot of their workflows. Joined with me is Sabrina Tirado. She's a customer success manager, within our retail vertical at Windfall. So she's actually gonna be walking through a Windfall application demo today and discussing some of the actual, you know, ways that, Windfall clients are using, some of this data in practice. As a quick overview, Windfall, we are a company based in San Francisco. We're, primarily a people intelligence and AI company. We're focused on giving go to market teams, you know, actionable insights and and the ability to leverage, you know, people data and predictive modeling and really supercharging all of that within, you know, marketing workflows so that you can more intelligently prioritize a lot of the resources on your side and ultimately drive, you know, stronger business outcomes. So just at a high level, we have about twenty million affluent US households within our database. We have just over a hundred million US households in general, over fifteen hundred customers, and over a hundred trillion total wealth captured in our database. And we'll get into a little more of a windfall overview overview here in a second, which leads me to the agenda. We will start with a quick windfall overview for those of you that are unfamiliar with our offering. I'll try to go through that pretty quickly. I know there's a lot of, current windfall customers here, but a lot of folks that really wanna understand, yeah, what we do in general. We'll jump into some affluent consumer trends and wealth insights, some more macro, insights that we're seeing across the, luxury buying ecosystem. And then we'll talk a little bit about some of the data driven processes for sales and marketing that, you know, we help folks implement. And then I think what's gonna be the most, fun of, part of this webinar is the actual application demo. So Sabrina is gonna be walking through, you know, the demo and going through some of these workflows live, so you guys get to see, what some of those things look like. And then, we'll save a little bit of time at the end for q and a as well. Just a a quick overview of today's webinar. I just wanna set the scene for, you know, expectations. Again, the target audience for this is really go to market professionals within the luxury retail industry who are really looking to get stronger insights on reaching their ideal customer personas and and translating that into, you know, achieving some of the goals for twenty twenty six from a data data driven standpoint. So what you what we will cover here is Windfall's mission, our high level product offering. I mentioned recent macroeconomic trends, that we're seeing impacting consumer behavior and potential spend, some high level wealth insights, and then, again, how you can start to develop some of these frameworks within the Windfall application and and within your own within your own business. Things that we will not be discussing today is, you know, getting more in-depth around Windfall's dataset, you know, how we model out wealth, our data science processes, and and we're not gonna get too in the weeds in terms of, like, our modeling approach. We're not gonna be speaking to your specific initiatives, or, like, windfall pricing, for instance. Those are all things that we are happy to discuss with you, you know, after the webinar on a separate call. So please feel free to reach out with us, to us after this webinar, and we'd be more than happy to set up time to chat with you all. So where I like to start, in in these conversations is really understanding, you know, this in setting the stage for this concept of the data maturity curve. We talk about this a lot, and we hear this a lot across a lot of retail brands is in terms of this overall journey that, in life cycle that brands go through from a a data maturity standpoint. So everyone's on different stages here. There's really no, you know, bad place to be or good place to be. It's really just where you're at. But where we see folks start is really in this, concept of establishing a foundation of of, of data driven work flows. Right? So this is where we see newer brands that might have, you know, not a lot of data on their customers or maybe some bad data. They're doing some manual client research. They're really just trying to cleanse and unify records within the database. Maybe they're starting to think about augmenting that database with third party data. But this is really that, setting the foundation of, you know, clean and strong data workflows, by having, you know, better data hygiene and better access to to high quality data. From there, we see a lot of organizations start to, you know, action and drive outcomes with that data. So that's where we see, you know, this process of, you know, building propensity models, lead scoring models, using data for direct marketing, whether that's direct mail or or or digital, net new audiences for omnichannel, more cadence retargeting. So looking at things like specific triggers within the database, and that's also, you know, dovetails into cross selling and upselling. But that's where we really see data start to be industrialized, within your ecosystem. And then from there, we can get, really, really tailored in terms of how, some of these organizations are leveraging data as they get further down this maturity curve. That's where we think about being more prescriptive and more proactive, using, you know, predictions within, the database, using triggers, things like, you know, major wealth alerts. Automatic engagement workflows is where we see a lot of brands using programmatic direct mail, which also folds into those trigger based workflows using more quantitative methods to develop persona developments, or looking at things like LTV modeling. But this is really where we see organizations start to skyrocket when it comes to actually, you know, like, getting to those later stages of the data maturity curve and and really starting to take off from a revenue perspective. So I mentioned I'll go through a Windfall overview. I'll try to go through this pretty quickly to save make sure we have plenty of time at the end for for the demo. But Windfall at a high level, you know, we've been at this for almost ten years now. The vision of Windfall is really democratizing access workflows and insights on people data. So why did why are we focusing on people data? There's really a major problem, with existing legacy consumer data solutions. The biggest one is is really and how this relates to luxury brands, there's not good detailed net worth data available. There just isn't. There's a lot of folks that are trying to do this, but the most folks are falling short, and I'll get into why in a second here. But one of the biggest reasons is that a lot of folks are using proxies for wealth. Right? So they'll look at things like household income, things like home value. Those are really bad proxies for understanding that the overall wealth and discretionary spend potential at the household level. Again, I'll get into why, a little bit later. And and then the last, sort of piece here is that organizations like yourselves, you know, luxury retailers are left to, you know, sort of pick up the pieces here. So it's up to you to sort of decipher, you know, what is good versus bad data. When you think about legacy data providers in general, they're really less than fifty percent accurate. And and this this speaks to that a little bit more, but, when we think about the average legacy data provider, they're saying, hey. You know, here's Jane Smith, and here's all of the fields we have on Jane Smith. So you're looking at these fields on the left here. You work your way down. Everything might look okay at the surface. But when you get into net worth and even a few other data points, but specifically in those upper stratifications of net worth, we've seen a ton of inaccuracies. So most folks will sort of cap out around a million or two and a half million, or they'll provide these broad ranges. We really know there is a unique, you know, net worth to be observed at the household level. It's really due to those legacy vendors. They're they're building all of that data off of, you know, survey data or census data. So they're extrapolating from small survey sizes. They're looking at geographic analytics. They're not refreshing data frequently. So most folks are doing annual or quarterly refreshes, so they're missing a lot of those, you know, major household changes that are happening every week. And as a result of that, this is actually from a study that Deloitte did. About twenty billion dollars is wasted every year due to inaccurate targeting, and that's just a US figure. So fast forward to today, Windfall, we are a data company. I mentioned this before, but, you know, we have about a hundred million US households. We're more well known for these twenty million affluent US households. And we partner with a lot of notable, you know, luxury, brands, and it goes beyond retail as well. You know, you have the fashion files, the visual comforts of the world, but then you're also just looking at, you know, yachting brands, private aviation, hospitality. We do a lot in financial services as well, and then we also support a large volume of nonprofits within their fundraising efforts. When we think about some of the workflows that retail brands specifically are are implementing with Windfall, it really comes down to, you know, three major buckets. The first is really identification or prioritization. Right? So who's your customer? What do they look like? Do you actually know who your customer is? This is where we really wanna understand and identify who that ideal customer persona is. And then go beyond wealth. Like, there's other attributes to be had and really understanding who these folks are. This is where our data science team comes into play. This is where we can start to start building predictive modeling algorithms to really, you know, stack rank these attributes and understand what's the most important indicator of, you know, maybe a conversion happening within your database. And what we preach at Windfall is, like, it's great to identify. It's great to understand, but we wanna actually put this data to work. We wanna weaponize this data, and that's where we think about this last piece, which is where we're using this data to be more strategic about how we engage with individuals. And that's, you know, looking at retargeting, reengagement workflows, as well as net new audience audience targeting as well. So as a result, you know, this is sort of the problem that we're solving is that you have these consumers. We don't really know much about them. We don't know if they've had recent liquidity. We don't know have an accurate picture of net worth. And so understanding who that target persona is right now is very difficult. And, you know, when folks are leveraging windfall, they're able to get clear visibility into a lot of those fields, like precise net worth, what like, somebody's propensity to actually, you know, purchase or engage in a specific way, you know, major liquidity or household events, as well as financial sophistication indicators. So I did wanna, take a step back and talk a little bit about, you know, what we're actually seeing from a a macroeconomic standpoint within the affluent consumer space. So the luxury market, has actually fundamentally shifted over the last few years. I wanna make sure we're all, you know, speaking the same language and on the same page about what that means for luxury retail businesses. A few specific things stand out here. First of all, spending is concentrated at the very top. Right? A tiny slice of customers is now driving nearly half of all luxury revenue, while at the same time, we're seeing about fifty million aspirational buyers just completely exit the global luxury market. So the middle is really thinning out, and the top, is really where the game is being played. Right? So the good news for this is that, that that US affluent demand is actually holding up very well against some of this global volatility. But here's some of the problems with that. Right? Only one in three luxury brands is actually experiencing growth. There's a lot of reasons behind that, but one, there's a major personalization gap. Some top tier VIP customer segments are reporting a decline in perceived service and recognition. Right? So they're not feeling like they're getting that personalization, that that high touch nurture, you know, engagement that they should be getting as as VIC customers. Additionally, performance marketing is at an all time, you know, difficulty peak. Audience precision right now is just a must and a need to have for luxury brands if they wanna grow efficiently. You just can't afford to be wasting spend on the wrong people. And so as a result, you know, AI has now penetrated every aspect of, you know, retail marketing, but AI is no good without, you know, high fidelity data. You know, what good is AI models if you're not feeding in strong precision data into those models for them to work off? So, that's where, you know, high fidelity data is, you know, the gold standard of driving scale, especially in an AI, driven environment. And, this chart will actually tell the story pretty clearly. If you look at twenty twenty one, VICs, which Bain defines as customers spending more than twenty thousand dollars a year, make up about one point five percent of the customer base and account for roughly thirty five percent of all luxury goods spending. By twenty twenty two, it's already at two percent of, customers and forty percent of spend. And looking at twenty twenty four, we're over two percent of customers and over forty five percent, of total spending. So this is pretty, unambiguous. Right? A smaller and smaller slice of your customer base is becoming responsible for a larger share of your revenue. So what that means practically is that misidentifying or underserving even one of these VICs has, like, a real revenue consequence. And the flip side, you know, if you can identify who your VIC is before they've reached that status, you have a huge opportunity to deepen that relationship early. So, like, that's what the problem that windfall is really here to to solve is, like, how can we identify these people early? Where are those VICs of tomorrow that we need to be engaging today? So we know who we need to reach. Right? We need to reach high net worth, high propensity spenders. The challenge is that the main tool that most brands have, and most brand brands have been using to find and retarget those people is really kinda falling apart. Right? Third party cookies, which have been powering digital targeting for years, are becoming increasingly unreliable, and and there's really three main figures here that we put to to tell that story. The first is around match rates. Match rates for third party cookie targeting are sitting around forty percent. So you're already missing around sixty percent of the people that you think you might be reaching. On top of that, up to sixty four percent of that spend is relied on cookies, and that can simply, you know, be lost due to deprecation, match rate issues. And brands that haven't found a replacement are spending nearly, you know, twenty percent more just to get the same results that they used to get. So we put this quote in from McKinsey. They put it pretty well. You know, nearly every aspect of media execution is now impacted. You know, if your strategy is still depending on cookie based targeting, you know, you're operating with a with a leaky bucket, you know, just to put it bluntly. So cookies are broken. You know, some brands are falling back on geographic proxies, right, targeting by ZIP code or income bracket, broad based targeting, using things as proxies for wealth, as I mentioned before. So, this slide actually shows why that's, you know, a pretty big problem. Right? Even if you're targeting the top five percent of income ZIP codes in the country, let's look at what's actually inside those ZIP codes. You know, more than half of households earning, are earning under two hundred k. Nearly thirty percent earn under a hundred k, and fourteen percent earn under fifty k. So you're basically paying to reach a lot of people who are not your customer. The pie chart on the right is, the the actual gut punch here. Targeting by ZIP codes only reaches about fourteen percent of truly affluent households. You're excluding about eighty six percent of the people you actually want. And remember what we just said on slide eighteen, wealth is increasingly concentrated in individuals, not neighborhoods. A ten million dollar net worth household can live on any block. Right? ZIP codes will never find them. That's really the gap that Windfall is filling here. Household level household level net worth versus neighborhood level proxies. So I don't wanna steal too much of Sabrina's thunder here, but what folks will do through working with Windfall is leveraging our our application. So we we've built this application over the past ten years to effectively leverage our data and our AI in one easy to use interface. So that's where you can start to think about, you know, modeling out audiences, whether it's retargeting high propensity individuals within your database or whether that's looking at the net new population that you really should be targeting across the US and deploying those audiences in an omnichannel way. So this is where we can think about, you know, taking a high value audience of actual households, you know, with deterministic wealth versus looking at, you know, pixel based identities or third party cookies and unifying that audience across, you know, your entire marketing channels. Right? So when we think about, you know, today's process of building an audience in Meta, building an audience in Google, those are fragmented channels. Right? So if you build a a lookalike audience in Meta, you build a lookalike audience in Google, the likelihood that there's gonna be a lot of overlap between those audiences or even that you're targeting the same people is pretty low. So where we help is actually unifying one high value audience, or multiple depending on some of the segments or creative that you're testing, across your entire marketing mix. So that's where we think about, you know, Stack Adap, Criteo, Meta, LinkedIn, Google, even direct mail. And Sabrina's gonna show a lot of these workflows shortly. And I again, I feel like I'm stealing some of her thunder here, but we're really trying to solve this problem of precision level household targeting so that luxury brands can grow, you know, efficiently and and and and smartly. When we think about, you know, how we actually integrate into these marketing channels, we've built out, you know, custom integrations into pretty much any place that you would activate audiences. So it's really gonna be this process that Sabrina's gonna show of, you know, building segments, and that, again, can either be looking internally, segments of your own customers, your leads, building segments of of net new, net new audiences, deploying those channel, channel campaigns. Right? So, we've built out all of these integrations into social programmatic direct, channels. And once we have those campaigns running, making sure those audiences are are receiving those ads. So that's where, you know, we actually, deploy those, custom audiences directly into the back end of your, paid media accounts. We really eliminate the need for third party cookies, within that targeting, process, and then we're gonna help you measure results, measure attribution, you know, throughout the process as well. Awesome. I am going to kick it over to Sabrina here, and she's gonna take over the screen share, and we'll walk through some of these workflows within the Windfall application. Awesome. Yeah. Thanks so much, Steve. Excited to get into how all of this works in practice and actually how we create, you know, segments both net new and based off of your data to actually activate this. So I'm going to take the screen share and pull up the Windfall application. So here is the homepage of the Windfall application. All of this will be customized based off of your personal data. So any, you know, overall match rate, all of the data that we have matched against your database, and then taking a look at a few additional insights of that population. For the focus of today's demo, I really wanna in discovery and campaigns. So I'm gonna go here on the left and get into discovery. So here's where all of these segments that are created will live, both net new and retargeting. For any of these segments, they can be deployed to campaigns. So as Steve was saying, any of your marketing destinations can be can be leveraged with any of these audiences. Want to go through a few examples of first some retargeting segments. So ways that we compare both your first party data and our windfall attributes to create custom lists with various use cases. So based off of their size, understanding people who are lapsed customers with the, you know, high propensity to spend given their network or liquidity trigger, taking a look at folks based off of their location, perhaps for, you know, actual, like, boots on the ground events, or taking a look at broader segments, people who are past customers and we know that they have a high net worth to potentially deploy for your marketing destinations. Also wanna walk through a few examples of net new segments that we can create based off of your data as well as some of Windfall's attributes to deploy to marketing channels. So I wanna dive into a few of these. I'd like to start here, taking a look at this first retargeting segment that I've created here. So just gonna click into this. So any of the segments that were on the previous page, you're able to click into. This one is based off of, individuals within, the customer's database that have a net worth of over ten million dollars and we're seeing that they don't have a loyalty tier. So here, when you're looking at this screen on this little eye right here, you can actually click into the construction of the segment itself. So understand how we are filtering this group. So taking a look at this, we're seeing that these are folks that have a order count of less than two. We have a net worth of over ten million dollars, and we're seeing that they don't have any loyalty tier. So what this is telling me is the ability for these folks to potentially be, you know, matched with a sales consultant, you know, more direct outreach with a person of, you know, this stature within your database. And as we see with their network, they have the propensity to probably spend a bit more. For any of these, segments, we'll be able to see a few additional insights. So first, the total wealth of this group. So out of the six thousand or so, individuals in this segment, we're seeing that the concentration of wealth is extremely high, two hundred billion dollars. We can also see their location by county. This is taking a look at their primary properties. So we can see where they're distributed throughout the US. A cool update that we do have rolling out in the near future is the ability to actually drill into any of these specific counties. So get a more in-depth look at, you know, these four hundred people in the Los Angeles area, for example. And then as we go down here, we can see the net worth density and average and median net worth of the group. So seeing the average as well as the median to kind of discount any of those ultra high net worth worth individuals that are probably within this segment. We can also see here some additional windfall triggers that provide us with additional insight into this population. So seeing that a very large percentage of of them are philanthropic givers, multiple properties, and so forth. In terms of any segment like this, we have the opportunity to action. So here on the top right is where we have a few different options. The first would be to activate a marketing campaign for any of our customers that are in Salesforce. We also have the option to directly deploy a Salesforce campaign. So that could be a net new campaign within Salesforce or joined with any, you know, Salesforce campaigns that are currently active. And then this last one that I wanna focus on here is export data. So based off of the size of any of these lists, we actually have the option to provide this in a concise list format directly back to you. So while we do provide ongoing enrichment, which essentially means that we're providing all of the, you know, matches that we have against your database directly back to you, if there's ever a use case for, you know, filtering on more specific segments and it's more actionable for us to have the SynaLis format to plug into for an email marketing campaign. Or we need this for direct hand to hand outreach with the sales team. We can export any of these segments here for the team to directly download right here within the application. To go over a few other examples, taking a look at this next one, looking at lapsed customers. So we're taking a look at transaction dates as well as net worth as a comparison. So taking a look at folks who have not transacted in over a year, we see that they either have a net worth of over ten million dollars or we see that they have a net worth of over three million dollars as well as our, liquidity trigger active, which is providing us with indication that a household has over ten thousand dollars in luxury spend, donation history, over the last three months, which is providing us not only with an indication that, yes, this household has the capacity to spend, but they are spending. We have that additional indication, that would perhaps, you know, be a good opportunity for, you know, outreach at this time with that person. Similarly to all the other segments here, we can see all of the insights based off of where these folks are located, the amount of wealth within this group, etcetera. We also have a comparison against the two groups, which you'd be able to see, you know, for any segment where you're creating it based off of two different groups so we can understand the comparison against those. But just to go back into discovery to go through a few other retargeting examples before I get into the that new audiences, we can also filter here by geography. So something that's really cool that we're able to do within the Windfall application is leverage all of the address data that Windfall has within our database to provide you with filtering tools to understand where folks within your CRM are located even if you don't have that address. So for an example here, what we're able to do is by leveraging Windfall's county attribute, we're able to filter by folks that we know within Windfall's database that have a property in Suffolk County where the Hamptons are located, and then filter by any of your other attributes as well. So for this example, we're taking a look at folks that we know are in the loyalty tiers for this customer as well as have a property within the Hamptons for a potential, you know, in person marketing event. So in terms of all of these segments, do you wanna review, you know, how these are created at the end of the day? So we go up in here into create segment, and this is where we'll be prompted to either create a net new segment, a TAM segment, or one with segment my data, which is what we'll be selecting for this first use case. Here in segment my data is where we will have the ability to add segmentation. So we'll have both windfalls attributes, all of our fields that we have enriched for you. So, anything from our wealth dataset, as well as a few additional attributes to filter by, as well as your first party fields. So those fields that are really important internally for capturing where someone is in the pipeline, where we know they've purchased in the past, what we have as their AOV, their total order amount, things like that that can help us really understand and activate specific populations within your database. So for this example, say we wanted to take a look at folks that have that are prospects. So these are people within this customer's database that exist within the database, but they do not have any transaction history. And we wanna filter this against folks that we know have a high net worth. And perhaps we have a specific event in a in the New York area. We could add filtering here Going to network. We have a variety of geographic filters, to understand where folks are are located, where their main property is, where they have any property. For this use case, I'll put in metro area, and we'll do the New York area and perhaps Washington DC, and then we'll select. So once we've created the rules for the segment, we go into next step, and this will prompt a question of data suppression. What this means is at a baseline understanding is do we want the people who are within our database who have transacted within this file or within this segment or not? Obviously, since we're taking a look at prospects for this, we'll wanna make sure that we're suppressing customers. However, for other other segments that are using, you know, any of your transaction history, we'd obviously wanna click don't suppress. So everyone within the segment is included. So we'll add that suppression, and then we give the segment a name and then click generate segment. And then after that has been clicked, all of these will, you know, live here with all of the other segments. So that's an overview into understanding how we can leverage both your first party data and when false attributes to slice and dice, you know, your data to find these, like, smaller populations that are, you know, worth activating. In terms of how we'd be able to download any of these as a list, I did just provide I did do one export here so I can show the team how we're able to download that. And then afterwards, I'll show you guys how we can create net new audiences as well as deploying those key campaigns. So for any of these, we'd go into export data. In terms of the enrichment profile, this is just a fancy way of saying what fields do you want in this file. If this is, you know, specific for a marketing automation, maybe we just need first name, last name, email addresses, and customer IDs. Maybe we want this for sales outreach, so we wanna make sure that we have, you know, all of the data possible on these records. So we have all of the, windfall profile, matches. And then, you know, there's all of these customizations that we can do just to make sure that this file has only the relevant data that the team needs. For this use case, we'll just click here on marketing automation, folder, file date name, and then just click next. And once we click confirm, there will be a new tab that appears up here where we can actually click on this and once it's processed, just directly download it right there. So that's retargeting segments. To pivot to net new and how we create those within the application, we'll still do that here within the discovery tab. So if we'll go here into create segment, we'd click up here into net new. Segment size, up to twelve million people. This can be, you know, whatever size we'd we'd like to constrain it to. Additionally, once, you know, we have implemented with the customer, we'll also have the option here to add a, add the marketing model. So what that means is we will take the data that our customer has and understand based off of that who your ideal customer is. And we'll work with you to make that definition. And then we'll take that definition, an understanding of what that profile looks like within your database and apply it to Windfall's database. So what that means, we can leverage Windfall's database to locate net new individuals who most closely resemble your ideal customer. So in tandem with the marketing model, we're also able to add additional layers of segmentation. So in addition, if we wanted to create this audience of folks who have a net worth of, say, over five million dollars, and we wanna focus in addition on top geographies just based off of where we this customer has store locations. So we wanna do San Francisco, New York, Los Angeles, and Miami. Next step. And then here is where we'll be prompted for data suppression again. The caveat here is if we're creating a net new audience, it depends on the customer's preference if we'd like to suppress everyone who's already within the database just to ensure that this audience is in fact purely net new. So for this use case, we'll click suppress my data and then click next step, and we'll give this segment a name. And then click generate, and the segment will generate. I've already created this as an example. So similarly to any of the retargeting segments, we're able to double click into any net new segments as well. So here, we can take a look at the overall insights of this population. So while this is constrained to records that have a property within one of these metro areas, this map is showing us where these households have their primary property. So we can understand, you know, how wide the geographic spread is of records that do have house households or any properties within those metro areas. Similarly to the segmentation breakdown for retargeting groups, we can also see here the network by bucket, understanding the, you know, net net worth concentration of records within this population as well as some additional insights about this group. Another thing that we'll show here when we do have a marketing model deployed is the breakdown of how closely the population resembles your top customer. So taking a look at how the model scores records within the net new audience against that ideal. So there'll be a graph here that essentially shows, you know, a bar chart for folks from zero to ten. And based off of that, we'll show, you know, what the concentration is of folks that score close list most closely to that ideal definition. And then in terms of activating these net new segments, we can create marketing campaigns out of any of these. So if we wanted to click into any segment, we'd be able to go up in here into actions and then activate marketing segment. So once we have this page up, we would give the campaign a name to start. In terms of campaign start dates, you know, if there is a end date in mind for a specific sale or initiative, we're able to add that. However, we can also have the option to make any campaign ongoing for the time being. We can just click today's date. This is also really helpful for Windfall's attribution because what we do is we take a look once audiences have gone live, and we'll examine any net new records that have come into your CRM and cross reference that against the audience to understand who within Windfall's audience came in as a lead and, you know, any of the conversions that do come from that. We also have the option to keep this campaign refreshed, which is a very cool feature that, you know, takes place on whatever, you know, cadence a customer would like. We would typically recommend monthly. But what this is essentially plugging in for us is Windfall's rebuild of our database. So while most customers will will typically want to keep, you know, campaigns evergreen, we're actually able to refresh them without customers needing to, you know, replug in audiences. And essentially what this does is on a weekly basis, Windfall rebuilds our dataset. Very unlike other data companies that are leveraging census level data, it's quite stale. We're actually taking a look at everyone within our database week over week. So we're adding new records. We are adding additional PII to records that we already have. We're also updating any of our recency attributes. So our understanding of anyone who has a liquidity event or some of our other attributes including recent mover and things like that. We're able to update that on a weekly cadence. And then what this does for the audience is on a monthly or weekly cadence, what we're able to do is take out anyone within the audience that, you know, meets this criteria, but they might look the furthest from your ideal customer. And then we're adding someone in who's not new to our database who looks more like that ideal. So it's keeping the audience with, you know, the latest and greatest of of folks that closely resemble that ideal as well as meeting the segmentation criteria. So for most customers, we'd recommend doing this on a monthly cadence just to make sure that there's not a, you know, reset of the learning phase on a weekly cadence. So we can do this, you know, on the first of every month for this use case and then just click apply. And then here are the segments that are included within the campaign. We also have the ability to exclude any. So say we have, know, another audience running in tandem and we wanna make sure that, you know, where you're taking a clean look at attribution and these folks aren't included within this campaign, we can exclude, you know, specific records. And then here we have the ability to add any of your destinations. So as Steve mentioned, we have integrations with almost any marketing destination that you could think of. And based off of what the team is actively leveraging at the moment, say that's LinkedIn and Meta or LinkedIn and Stack Adopt, we can select those. And then this is where these audiences will eventually live. And then in terms of in terms of the last steps, we're able to do a few things. The first is select size per destination. So say that's based off of goals within each platform, we can customize what we want the actual audience size itself to be in each each destination. We're also able to add a campaign holdout, which really helps us measure the incrementality of marketing campaigns. What this will do is take either a five or ten percent chunk out of the audience and then not market them. So what we'll do is examine the efficacy of the ad against who has been exposed to it versus those who didn't, who still meet that same criteria. And we're also able to add existing holdouts to any of your audiences. So we keep, you know, the same holdout group, understanding, you know, how people with a high net worth in these areas who are not exposed to your ads versus those who are, you know, at the end of the day become leads and then convert. And then we just click create campaign here, and here in the campaigns tab is where all of your campaigns will live. In terms of additional insights, we also have the ability to look in and see the performance of marketing within the app. So once we've kicked off, we'll have the ability to see attribution within the application. This is just not uploaded at the moment, but this will be able to show us some additional insights into how marketing is performing. Just gonna go back into discovery for a moment and walk through a few additional examples of ways that we could activate. So, obviously, based off size, we want to, you know, understand what the best use case is for a segment. So for these smaller retargeting groups, maybe smarter not to directly activate within platform just based off of potential costs. But what we can do for some of these larger retargeting segments is activate them for campaigns as well. So here for any retargeting segments, we can also do the same thing and export or, create marketing campaigns for any of these as well. Okay. And then the last thing in the application that I wanted to walk through is here an overview where we're able to first take a look at how we're connected with your data as well as destinations that we have active. So for any customers based off of how we're providing data, we'll have a few insights here into the frequency of any syncs as well as if we're providing files, you know, when we've provided the the last records to your team. And then here in destinations is where we have the ability to track what destinations are being active. Here in destinations is where you're actually able to, you know, search by any of the destinations that we have integrations with, which is a lot of them, and make sure that, you know, these are connected. And then once we create any of these campaigns, what essentially will happen is within one to three days, they will populate within your ad platforms for the team's usage.