Why Wealth Data is Really Hard…But You Need it In Your Workflows

Thanks for joining our webinar today on why wealth data is really hard, but you need it in your workflows. Looking forward to jumping right in. So to give you a sense of who's speaking today, my name is Kathleen Atkins. I'm our VP of marketing here at Windfall, and I'm joined by Tyler Dillard, who's one of our amazing solution engineers. And, we will be, talking to you about this topic today. So to give you a quick overview of what we're going to be talking about today, I'm gonna start with a very quick overview of windfall to give you some context on our perspective, and then we're gonna jump into some macro trends around, wealth insights, then jump right into the meat of why wealth data is really hard and how you can leverage that data in your workflows as well as some q and a. We do encourage you to ask questions along the way in that q and a feature in Zoom, so please ask questions at any point that you have one. And, let's go ahead and jump right in. So we'll start with just a few minutes of context about Windfall. And we were founded in twenty sixteen, and our vision is to democratize access workflows and insights on people. And people data for us means two things working together, wealth profiles and career profiles. Most vendors give you a piece of one of those things or wealth ranges that are too broad to act on, but we built Windfall to combine both and keep them current and refreshed every single week. And we did this because when we entered this market, there were existing problems that are still prevalent today. So if you look at net worth or wealth, there's really not great data available. There might be a large range, or it might be capped out at a certain point, but finding good wealth information historically has been hard. And folks are utilizing things like household income or home value. And, well, like, I'm based here in San Francisco. Right? So that would be pretty bad to do here where everybody would be considered a millionaire based off those but that's really not true at all. And finally, even with an with AI, organizations still have to decipher good versus bad data, and that's pretty tough. So how do you know that these data points are actually accurate, and can you trust it in your overall workflows? So we built a data asset that tracks over one hundred million households across the US, and about twenty million of those households have a net worth of one million or more, which is how we categorize being affluent. And we work with many commercial go to market marketing and clienteling teams across industries like luxury retail, travel and hospitality, wealth management, and beyond. And we have a nonprofit arm that works with some of the nation's leading nonprofit universities and health care systems. So how do we put this to work? At a high level, we empower you to with really accurate people data. And that starts with identifying and prioritizing, who teams should be focusing on as their ideal customer profile. And Windfall also helps you to really understand your database. So this could be additional segments, cohorts, personas that we're able to glean more insight on through analysis and segmentation. This helps us determine data strategy based on where you're winning. And finally, all of this data is most useful when it's being fed into workflows. So taking all these insights and leveraging them to engage with your customers with personalized messaging, modeled marketing audiences, and plenty of other use cases as well. So the last slide on windfall for now is about the different ways that we can work with organizations. You could have a multitude of different databases or datasets, and you could put that into windfall. And then we have a lot of outcomes that are driven from that, whether it's segmentation and analysis, how you think about upsells, cross sells, or building predictive models for any of your acquisition methodologies as well. So there's a lot of different ways and outputs that you can leverage this data for. But let's go ahead and jump into those macro insights. So we wanted to provide context on the broader macroeconomic trends that are impacting where we are today, particularly among affluent households. Over the past couple of years, wealth has become increasingly concentrated at the top of the market. Today, roughly eighty percent of total US wealth is held by households with a net worth of one million or more, which is shown on the right. And for those who've been following our webinars, this is a continuation of a consistent trend. We've seen this steadily increase over time, reinforcing the growing importance of understanding and engaging with those high net worth audiences. And this dynamic isn't just reflected in wealth concentration. It's also showing up in how different segments are saving and spending. So since the pandemic, for example, the top ten percent of earners in the US have accumulated significantly more excess savings than the remaining ninety percent. As a result, that group now accounts for roughly half of all consumer spending, which you can see on the left. And we see the same pattern play out in Windfall's client databases as well where top earners and high net worth individuals tend to convert at a significantly higher rate and drive a disproportionately large share of total spend across overall revenue. And as we look ahead to twenty twenty six, these trends have become even more important to keep in mind. Today, roughly half of all US consumer spending is driven by top earners. And in many discretionary categories, seventy percent of growth is coming from affluent households. The those are those ones with one million or more in net worth. Not only are they driving the majority of spend, but they're also growing at a significantly faster rate than the average US household. So from a strategy perspective, this makes prioritization quite critical. When allocating time and budget, focusing on these high income, high net worth segments is where we consistently see the strongest returns. Importantly, this isn't isolated to a single industry. We see the same pattern across retail, nonprofits, travel and hospitality, as well as financial services, alternate investments, and wealth management. And at the same time, we're seeing a significant transfer of wealth underway. Many of you are likely familiar with that concept of the great wealth transfer. This is something Windfall's been tracking since its inception. And while the silent generation and baby boomers still hold the majority of wealth today, that wealth is beginning to shift. And we're seeing assets move into the hands of Gen x, millennials, and even Gen z, signaling that there's meaningful generational transition that's going to continue to reshape how wealth is distributed and how it should be engaged with going forward. So as we think about the timing and key life events, this becomes especially important. It's critical to identify when wealth transfers from one generation to the next, whether that's to children or grandchildren. And there's a portion of that wealth that will go to philanthropy. A significant portion is gonna pass directly to those heirs. And from a data and strategy standpoint, this reinforces the need for precise up to date net worth insights embedded in your workflows. It's not just about identifying who is wealthy today, particularly in high ultra high net worth segments. It's about anticipating who's about to become wealthy and aligning outreach with these moments of transition, whether it's inheritance, liquidity events, or other major life changes. That's where we see really great opportunity to engage at the right time to drive really strong outcomes. So as we look ahead to the next ten to twenty years, the great transfer of wealth creates a clear set of actionable workflows for teams to implement. One key workflow is prioritizing high and ultra high net worth households. With the top two percent driving nearly half of all wealth transfers, teams can build targeted outreach programs focused on delivering bespoke high touch experiences to this segment. Another is developing strategies specifically for women as emerging financial decision makers. There's nearly forty trillion expected to transfer, to widowed women as an example, and this includes identifying these individuals within your data, tailoring messaging services and experiences accordingly. And a third workflow centers around next generation affluent consumers. So as Gen X and millennials inherit about eighty five trillion, organizations can build segments and campaigns aligned to their preferences, which are digital first engagement, sustainability, modern wealth management approaches, for example. And finally, there's a broader workflow around multigenerational engagement. This includes tracking family relationships, identifying wealth transfer moments, and aligning outreach with key life events to build long term legacy driven relationships. Taken together, these workflows help to operationalize macro trends into concrete actions, improving targeting, personalization, and ultimately improving long term value. So correctly identifying and capturing these high net worth and ultra high net worth households who are driving that disproportionate share of our economy is critical to driving success in the coming years. But that all relies on high quality data at the household level. And there's a Deloitte study that was published that shows that those legacy data providers were less than fifty percent accurate. So let's take a look at why that is. So we have this example here of Jane Smith, and this probably parallels experience you've seen to a certain degree where we see that certain things are accurate or close, her gender, home value, type of home, maybe occupation, but other things are way off, especially when it comes to a net worth figure. The reason being, these legacy providers are primarily built on survey and census data. That's often aggregated at a high level like a ZIP code. And if you look at San Francisco, like I mentioned, at a ZIP code level, it would look like everyone is a millionaire, but we know it's definitely not the case. And each household in that ZIP code is different from their neighbors and different from their neighbors, and they're all going to have a different net worth and other attributes than their neighbor across the street. So the other problem is timelines. If you're relying on the census, that data can get stale fast. Data from legacy providers might get updated annually or maybe quarterly, but the world changes a lot faster than that. If you look at February twenty twenty to April twenty twenty, a household is gonna look very different from a wealth perspective, and that's just not the type of thing that these other traditional vendors are capturing. So, ultimately, this leads us to targeting the wrong people or wasting a lot of money. And Deloitte, again, showed us that is upwards of twenty billion annually based on actioning that, inaccurate data. So here's how we can start to think about when it comes to choosing the right data layer. On the left, you have your CRM. This is where all your customer activity lives. The question is, what kind of data are you layering on top of it to drive decisions? Option one is more like a traditional data provider, lower cost, typically sparse, incomplete, or lacking context. And you might get a few attributes, but not enough to drive meaningful segmentation or action. Option two is, like, a higher fidelity data layer like Windfall, where the focus is on depth and accuracy and context, more complete profiles, more frequent updates, and stronger match rates to ultimately translate into more actionable insights. So the trade off isn't just cost. It's whether your data actually enables your teams to prioritize the right people, personalize outreach, and drive revenue. And that's really the goal here, providing the context that your teams need to execute effectively. And the quality of that data directly impacts those outcomes. So on the left, the lower, is the lower context data provider. We're missing key information, like net worth is understated, job level is unknown, and that creates some uncertainty from the start. And that uncertainty flows through the entire workflow. You get maybe qualification, generic drip campaigns, incorrect outreach, and ultimately poor segmentation. The result is a long sales cycle and a weaker client experience. Whereas on the right, with higher fidelity data layer, you have a much clearer picture of the individual, accurate net worth, confirmed philanthropic activity, and divine job level. And the clarity leads to accurate qualification, more personalized outreach, and routing to the right team from the start. So the results have faster timeline, better experience, and ultimately more closed deals. So the takeaway here is pretty simple, better context upfront, compounds across each step of the funnel, and materially improves outcomes. So now I'm gonna hand this over to Tyler who's gonna walk through in detail why wealth data is so hard. Yeah. Thanks so much, Kathleen. So let's get into it a little bit. I really appreciate you covering how important that is right now. But let's talk about why this wealth data is so hard to really wrap our arms around. So, historically, there's been really a plethora of data available. There's more data today. There's gonna be even more tomorrow, information that we could look at, that we can pull in, that we can access, but it's extremely challenging for a couple of reasons. And we we look at those challenges as really the four Versus of data. So the first one is just, as I mentioned, the sheer volume of data that's available. There is a ton of historical data out there. There are even more places that you can pull it from. And in order to aggregate everything from all of these sources, you need to have a a pretty good scale of infrastructure in place to even take the data in. The the next v that we think about is variety. This data can come in all different shapes and forms. We have structured versus unstructured highlighted here. You know, thinking about an Excel sheet, a very structured transactional data that's coming in. Right? You know what each column means. Everything's filled out. It's pretty clean. That's just an example of structured data, whereas unstructured data could be open form fills that people filled out by hand, where anything can be in the box, and you have to make a lot of sense out of it. Right? This means that when it comes to processing all of the different types of wealth data and other data that we might wanna look at, we have to understand that it's gonna come in and look wildly different from from different sources. The third v that we think about is velocity. Kathleen mentioned this earlier, but this is really how quickly the data is moving. Think about the stock market and about property transactions. These things are moving on a on a weekly basis, if not faster. You have to keep up with this information and how quickly it's being created, with how quickly changes are being introduced to the data, and still be able to process it at scale so you can take action on it. And the last v is veracity. This is how clean the data is, how much you can trust it right out of the box. Not all data sources, as we've kind of discussed here, are created equally. I'm sure that everyone here has experience with dirty or inaccurate data, and that's where this veracity comes in. There is some data that you might want to do additional cleaning to or take additional transformations on, and there's some data that you might wanna take at face value. So understanding which data source falls into which bucket, again, adds another layer of complexity and makes this all just really challenging. Talked about these issues at a high level, but we're gonna dig into a few of the challenges that we face around these areas with specific data sources when we're building our net worth model and why that wealth data and getting down to a precise net worth figure is just so difficult. But we're gonna talk around a few things. Real estate data, as luxury asset ownership, like boats and planes, and then finally, some SEC data to just illustrate some of these problems. So starting with real estate data, most platforms and providers rely on off the shelf AVMs or automated valuation models to estimate home values. These models can be useful at a high level. They give you a sense of property value, which can help us approximate equity or list price. Platforms like Zillow have really popularized this approach with publicly available estimates. And in aggregate, these models look fairly accurate. For example, median error rates can be as low as about one point eight or two percent, but that's primarily for homes that are currently active that are actively on the market today. The challenge is that on market homes actually represent a really small share of total properties, especially in, you know, a slower supply constrained market. And for the vast majority of off market homes, these AVMs really struggle. They rely on limited or outdated public data, and they often can't fully account for property specific details. And as a result, these valuations can be meaningfully off, you know, particularly when you're trying to understand true asset value. And, of course, when it comes to wealth, that homeowner equity, you can really see that dynamic on the right. For on market homes, estimates tend to track closely with actual sales prices. But for off market homes, the gap really widened significantly, which creates real challenges if you're relying on those legacy data providers or even, you know, cutting edge AI tools that are built on top of these same kind of legacy AVM models. When we look at this at the state level, you know, using Zillow's own published analysis of their model, we can see this really isn't an idle isolated issue. The inaccuracies are consistent across the US rather than just being concentrated, in a few states. In fact, a meaningful share of homes are really significantly misvalued. Nationally, almost seventeen percent of homes have estimates that are more than twenty percent off from their actual sales price. And this really matters because homeownership is a major component of overall wealth. If the underlying property value is off by that margin, it really materially impacts how we can assess someone's net worth and their capacity, whether that's for spending, investing, giving, or assessing their potential AUM. So Zillow arrives at these figures by backtesting their estimates against actual sales prices once homes are listed. And even in states where data availability is relatively strong, you know, we still see large error rates. Only one of the top states with the highest inaccuracies is a nondisclosure state where, you know, limited public data would typically explain weaker performance. So the takeaway here is pretty straightforward. Even by the provider's own reporting, these models have really meaningful limitations, and that lack of precision cascades into how you understand and actually act on wealth data. You know, if we're manually looking up a a constituent's constituent's home or a prospect's home or something like that to assess their potential net worth and overall capacity. To that point, at the individual property level, these limitations become even more pronounced. Public datasets don't consistently capture every parcel or reflect all changes to a property over time. At the high end, in luxury real estate, this is especially true. It is difficult to find reliable benchmarks because in many cases, these homes, they're not transacting they're not changing hands frequently, and the property itself may have or or very likely changed significantly since the last sale. You know, what was once a vacant lot could now be a fully developed estate, but the underlying data hasn't caught up to reflect that transformation. So looking at this example, the property is listed at forty two million dollars, yet there's no reliable Zestimate available to benchmark its value. And if we turn to public tax tax assessments, those valuations come in, significantly lower, which raises a fundamental question. Right? What is this property actually worth? So this is really highlighting that challenge of if we're relying on this incomplete or really outdated data when trying to accurately assess assets, especially for our highest value consumers or prospective clients. Clients. And to make things even more complex, regulations at the state and the local level can significantly impact the data available to train these models and to train our models. So in nondisclosure states, for example, you can see them highlighted here. Home sale prices are actually not publicly reported, which makes it much harder for these models to establish accurate comparable sales and can lead to us, you know, missing opportunity when we're trying to assess that wealth. So without visibility into what similar homes nearby are actually selling for, it becomes really difficult to benchmark value. And without strong comps, the accuracy of those estimates really degrades quickly. So in those markets where public sales data is limited, these models, these legacy models face a really fundamental constraint, which makes it even harder to reliably assess property value. I'm gonna get even more granular here because even at the city level, you know, national state to the city, there are a few additional factors that can further limit data accuracy, particularly in some of the most affluent and critical markets like California and New York. So in California, for example, prop thirteen caps annual increases and assess property values at two percent. But in reality, home values, especially in those critical markets like San Francisco or LA, often grow much faster than that. But the public assessment data only reflects that capped increase, which can lead to properties being significantly undervalued and taxed in public records. New York, meanwhile, presents a different set of challenges. Many properties are using vanity addresses. For example, a building may be listed on Park Avenue or Madison Avenue for Prestige, even if that that's not the true underlying address. This can create a ton of insin inconsistencies when trying to match and validate that property data. And additionally, many records in New York don't include unit level detail. So in a market that's really dominated by multiunit buildings, it can be really difficult to determine whether an individual owns a single unit or an entire property. And, of course, that has major impact on accurately estimating underlying assets and net worth. So taking together, you know, especially if we expand this lens to a national scale and look at all the different local nuances of different cities and different markets, these really further highlight just how difficult it can be to rely on public data alone to accurately assess real estate assets. So with all of that in mind, Windfall has developed a a more advanced, really multimodal approach that we've backed by significant investment in research and development to more accurately estimate property values, which is, again, such a critical piece of wealth in the US. We built models tailored to different data environments, including nondisclosure states where that, again, that sales data is so limited, as well as markets where comps and transaction history are more readily available. But as a result, Windfall's model is achieving a median error rate of just point six eight percent. And importantly, that includes both nondisclosure states and off market properties. So this is representing just a meaningful improvement over publicly available AVMs, automated valuation models like a Zillow, for example. So the benefit here is more accurate, more reliable inputs into overall net worth, just giving you a lot more confidence as you prioritize and engage with prospects. But that said, real estate is just one piece just one piece of the puzzle. It can represent a significant portion of someone's wealth. It's really only part of the broader financial picture. So for the average household, property ownership is pretty straightforward. If someone owns a primary residence and a second home, they are typically listed directly on the deed of those properties. But as you move into the high net worth and, more importantly, ultra high net worth segments, things become much more complex. These individuals are often using, as I'm sure many of y'all may know, trusts, LLCs, other entities for estate planning and asset management. And as a result, ownership is obscured, which makes it really difficult to determine who actually controls a given property and to accurately roll these assets up to the correct household. On the right is maybe the most extreme example with best Jeff Bezos, whose real estate is held across a network of various trusts and entities. But even with a really highly visible individual, building a complete picture of these holdings requires significant effort. And while someone like Bezos can be easily identified through public sources like Forbes, the challenge is much greater for high net worth individuals, As I'm sure many of y'all know from trying to do manual research, it's it's even harder for folks without a public profile. So this is where those traditional approaches in terms of manual effort or traditional data vendors really break down. It makes it hard, makes it really difficult to fully understand asset ownership and the the full picture of total household wealth. So far, we focused on property value, but, you know, debt is just as important when you're trying to understand true net worth. At a glance, two households can look very similar based on total property value alone. But once you factor in outstanding debt and loan to value ratios, the picture changes pretty quickly. So for example, a household with a two and a half million dollar home and no debt has a very different capacity than someone with a similar property value, but a high LTV and significant leverage. This is where a lot of traditional approaches fall short. Valuations alone can be misleading without understanding debt structure. They can overstate someone's actual financial capacity. And importantly, owning property doesn't necessarily mean that wealth is accessible. Equity matters, but liquidity is what's going to drive action and conversion. So the the takeaway here is just that to accurately assess capacity, you really need both sides of the equation, assets and liabilities, and not just surface level valuations. Another strong signal of wealth is plain ownership, something that a lot of organizations that Windfall works with are are interested in understanding. Right? There's data available from the FAA that can help us connect the dots, but it becomes complex really quickly. On the right is an example from an from the FAA registry. Each aircraft is tied to a serial number along with details about ownership, registration type, some other attributes. The challenge is really how ownership is structured. Aircraft can be registered to individuals, corporations, LLCs, partnership. You see co ownership, fractional ownership models. So very quickly, it becomes difficult to determine who actually owns that asset and how much of it they own, which raises the key question. Right? How do you accurately associate this signal to the right individual and make it actionable? Or do you just ignore it because of the complexity? And this is where data quality and identity and entity resolution really matter. This becomes easier to understand when we walk through just a specific example here. In this case, this aircraft is registered to an LLC. At first glance, you know, that might seem like a dead end. It's not directly tied to an individual or household, but you can go a level detail deeper. If you look up the registered address for that LLC, you may find that it maps to a residential address. That opens the door to linking the aircraft back to a household, but it doesn't happen in a single step. You need to bring in additional data sources. For example, you might use secretary of state filings to connect the LLC to an individual and then tie that individual back to a household. But just the point being, what initially looks like a simple dataset actually requires triangulation across multiple sources to accurately attribute ownership. And without that level of resolution, it's very difficult to sir turn a signal like plane ownership into something that's actually actionable. Another signal we often look at is boat ownership, but not all boats are created equal. On the surface, seeing that someone owns a boat might suggest affluence, but when you dig in, there's really a wide range. There's smaller recreational runabouts to large, of course, luxury yachts. And you can see that here. A ninety foot yacht is a very different signal than a twenty five to thirty foot fishing or leisure boat. The usage cost, the ownership profile can vary really dramatically. There are also differences in how these vessels are used, lake versus ocean, leisure versus commercial, further impacts how indicative they are of wealth. So similar to plane ownership, the signal itself isn't enough. You need that context, type, size, usage to determine whether it's actually meaningful in assessing affluence. Without that, you really risk treating very different households the same from a segmentation and a targeting standpoint. Lastly, looking at SEC data, this highlights another challenge around fuzzy matching and limited information in the public record. For example, we might see a filing for Michael Smith listed as an SVP of Publix. On its own, that seems useful. But when you zoom out, there are just hundreds of individuals with that same name even in a single city like Lakeland. And without strong identifiers, it becomes very difficult to confidently match that record to the correct individual. Details like a PO box don't really help much either, so you're often left with a lot of ambiguity unless you validate against additional data sources. This is where simply relying on name based or, in other words, fuzzy matching, which is really common across many providers, can lead to incorrect associations. Instead, you need to combine multiple datasets to accurately resolve that identity and tie that record back to the right household. There's a similar challenge with ownership context in SEC filings. Take someone like a a venture partner involved in a financing event. You know, they may appear in filings both as an individual shareholder and as a fiduciary representing a firm. From public data alone, it's just not always clear whether those holdings are personal or tied to the organization, and that distinction is critical when you're trying to assess true individual wealth. So, again, the takeaway is that aggregation alone, it just isn't enough, and you need context and proper attribution to build an accurate picture of household level assets. Just gonna provide one final example here. In this particular case, we see an individual registering Zendesk stock. On the surface, that looks like a clear picture or signal of personal ownership, it come becomes more complex when you realize that this individual is also a partner in a fund, which raises an important question. Is this stock held personally, or is it owned by the fund they're affiliated with? That distinction is really critical. If it's personal, it should be attributed to the individual's household. If it's tied to the fund, it shouldn't be treated as part of their personal wealth. So this is another example of why context matters. If we're not properly understanding ownership structure, it's really easy to misattribute assets and overstate or understate a true net worth. So to get into how you know, we understand why this data is so important, what we're seeing in the in the broader economy right now, some critical events that are coming up in the next, you know well, that are happening now, really. So how do we action on this data and ingest it into workflows? I'm gonna share some some customer stories here of how folks have leveraged this data. But first, I think a helpful context is just understanding what we think of as the the data maturity curve. You know, what we're seeing on this slide is really a maturity curve for how well data is able to support data, AI, and action. The x axis is maturity. The y axis is business value because the more mature your data foundation is, the more data and AI can actually do for you. Windfall here, we consider this in in three stages. Establish is where most organizations start. They're working out of their CRM. They're segmenting on transactions or engagement. You know, maybe some folks are starting to layer in third party data or enrichment. Many folks that we work with are who are, you know, starting with basic segmentation and reporting, you're likely in this area. Drive is where you start getting more value from that same data. Smarter segmentation, better reporting, tailored direct marketing, and predictive models. And then lastly, orchestration is where data begins to trigger action automatically. AI can generate content. Systems can alert teams when something changes, and prescripted work prescripted workflows really begin to happen here. The key point is that this isn't about just good or bad answers. It's about identifying where data can start doing more of the work in your organizations. And the great thing is that Windfall assists our clients regardless we're able to assist our clients regardless of where they are on this stage. So to bring this all together, here's a quick case study with a a top asset manager that we work with. The objective was really straightforward, overlaying Windfall's data on investable assets onto their existing client base to identify opportunities to grow their five million dollar and up client base or five million dollar and up segment. On the results side, they saw sixty percent increase in adviser booking rates along with one point two billion in new AUM captured from an initial, you know, broad campaign. The way they achieved this was by enriching over nine million client records with household level investable assets data. What that did was give them visibility into share of wallet across their existing base. And from there, they were able to segment more effectively and prioritize outreach, allowing advisers to really focus on those highest capacity households. So instead of really broad, undifferentiated campaigns, they were driving targeted engagement tied directly to opportunity, which resulted in measurable growth both in adviser activity as well as assets under management. This is another example, with an alternative secondaries platform that we worked with. The objective here was to reengage accredited prospects who had dropped off during onboarding and expanding acquisition of net new qualified investors. The approach here was to layer in a similar enriched data as the previous example, but modeling as well. That was both to inform internal workflows as well as build net new audiences that they activated across digital channels, paid search, meta, and so on. So as a result, from that enrichment side, they surfaced over a hundred and twenty thousand accredited investors within their CRM and identified nearly thirty thousand incremental qualified purchasers. From there, they were able to activate those audiences, again, on paid channels, driving over twenty seven users directly or twenty seven hundred. Big difference there. Twenty seven hundred users directly from windfall powered segments and ultimately generated, you know, almost four million dollars in buy volume in that first three months. So the theme holds. Right? Better data, more accurate data, and segmentation is leading to more efficient reengagement, stronger targeting, and, again, bottom line, right, measurable revenue impact. Lastly, this example shows how a large wealth manager that we work with incorporated windfall into their prospecting strategy. The objective was to enhance direct mail campaigns by layering in high net worth prospect data. From a workflow standpoint, they start with their existing prospect lists. They use Windfall data to suppress known contacts and and avoid any overlap there. And from there, Windfall identified those net new high net worth prospects to to expand their reach. They then enrich those records with our precise wealth data and evaluated LTV and capacity within the Windfall audience, which allowed them to prioritize who to target. On the result side there, these prospects had we're we're really optimizing for those those bottom line metrics. Right? So two and a half x, two point six x higher median investable assets compared to their broader audience. Beyond that, this opened up additional opportunities. Things like propensity modeling, incorporating life event triggers like retirement or liquidity events like receipt of inheritance or the sale of a business, just further enriching leads to demonstrate value upfront, personalized outreach, and prioritize from a timing perspective. The takeaway here is that more efficient prospecting a more efficient prospecting engine, whether that's better audiences, smarter targeting, and, again, bottom line, higher value outcomes. Alright. Wrapping up, just a few key takeaways here. First, in today's environment, as Kathleen covered, it is critical to identify and engage high net worth households. They're driving a disproportionate share of wealth and spend, and who those folks are is soon to be changing pretty significantly. Second of all, not all data is created equal. Most providers, many providers are missing the mark, and they're relying on incomplete or extrapolated data that leads to poor decisions on our end. And then third, even with all that data available today, actually assembling and making sense of it is incredibly complex, especially at the individual or the household level. And then finally, just to capitalize on this opportunity, it's not just about having the data. It's about operationalizing it. Implementing data driven workflows is really what ultimately drives better targeting, invasion engagement, and outcomes.