Starting and Optimizing Your Direct Mail Program

Thanks for joining our webinar series today. We're gonna be talking about starting and optimizing direct mail programs. And to give you a sense of who's speaking today, I'm Kathleen Atkins, our VP of marketing here at Windfall, and I'm joined by JD, who is our VP of product. And we're going to try to keep today interactive. We'll have some polls. We welcome you to ask questions throughout in the, the q and a feature. You can, ask those directly or anonymously, but throw us any questions you have throughout, and we'll also have a section at the end to jump into that specifically. Before we get into the agenda, I wanted to start with an example of direct mail program before we get into data, because these numbers start to mean a lot more in context. And today, we're gonna have people from different fields across nonprofit, retail, travel, hospitality, and financial services here. So I've got a mix of different case studies throughout from different sectors that we serve. So this first one is Big Sky Youth Empowerment, and they came to us wanting to drive more out of their overall direct mail program. They were using Windfall as the foundational dataset for targeted outreach when they started, and they ran a campaign that actually generated four times the gross revenue of the campaigns that they were running before they started with us, and got a two and a half times the response rate. Their business development manager talked about that best and said, even in what was a really difficult fundraising environment that they were in, it ended up being their biggest fundraising year ever. And that's not with better creative or better list, or budget, that's the right data getting the right piece of mail, or program to the right person. And we're gonna walk you through how to do that today. So a quick overview about how we've structured the session. So, I'm gonna give a very brief overview of Windfall, just enough to have context so that the rest of what we share makes sense. And from there, we'll get into why Direct Mail is having such a great moment right now and how to build data driven campaigns that perform well. JD is gonna talk through direct mail in more detail and share we'll share some customer examples at the end as well. And like I mentioned, we'll be running some polls. So let's kick off with the first one. Would love to hear from you about what brings you to this webinar today, whether you're just getting started or optimizing, would love to hear from you about that. We'll just give a quick moment here so we can get a sense of where everybody's at in their journey. Okay, just another moment here as these answers fly in. Okay, amazing. So I'm gonna go ahead and share these results with you. So you can see that there's about half of people who are just getting started. So we'll definitely take that in mind with what we share today. And then a good other chunk around segmenting database and want to tune the targeting, and not sure where to start. So, we've got something for all of you, and we'll go ahead and dig into what's next. Thank you so much for participating in that. So, as you're thinking about the answers to those questions, it really can be framed in looking at a data maturity curve. So, as we look at this slide, I'm gonna show you a chart where the X axis is maturity, which doesn't mean like how long your organization has been around, but rather how sophisticated your use of data is. And the y axis is gonna be the business value. So the further right and up, the more data is actually doing work for your business. And we break this into three stages. So the first is establish. And this is where most organizations start. So you're working out of your CRM, segmenting on transactions, maybe starting to layer in some third party data or enrichment. And if you said in the poll that you're just getting started, then this is probably the phase that you're in. The second stage is drive. Now this is starting to do more with data, smarter segmentation beyond transactions, tailored direct marketing, better reporting, starting to use predictive AI. And if you said you're segmenting today, but looking to better target, you're probably right here in this stage. And then the next level is orchestrate. And this is where data starts to trigger action automatically, like where a lead comes in, something changes in a prospect's life, and your data and your system responds. And then you're combining that predictive AI with generative AI to create content based on those signals. And that combination is what we start calling prescriptive marketing. If we do A and execute content with B, then we can expect result C. So most organizations aren't neatly in one place. You might be farther along in one area than another. And the goal here is there's no such thing as being behind. It's about asking where can data start doing more for me today in my workflows that exist today, and what's coming next? So a lot of what we're gonna talk about today is going to follow this principle. And before we jump into that, I'll give that quick overview of Windfall that I mentioned, just enough context to get us started. So we've been around ten years now, and our vision is to democratize access, workflows, and insights on people. And people data means two things working together for us. So, wealth profiles and career profiles and other life events. So, most vendors will give you one or the other, or with wealth ranges that are too broad to act on. And we built Windfall to combine all these elements, keep them current, refreshed every single week. And for direct mail, that precision and recency and data is really critical. As you start looking at the cost per piece, really can't afford to be guessing. And guessing is a lot of what has been the problem with legacy providers in the space. So, this is probably the most important slide in my quick overview, so spend a moment here, where some of these vendors were built on survey and census data. So concepts from 1950s where you and your neighbor may have looked pretty similar, and that's just not the world we live in today. So take a look at this example of Jane Smith. The legacy vendor would have their net worth at five hundred thousand to a million, but in reality, she's worth eight point two million. And if you start targeting the same approach with that level of discrepancy, there's a lot that could be tuned there. And imagine that you've built your entire segmentation around data that's looking about fifty percent inaccurate here. You've got a lot of things that could be better tuned. So, that's not just about inaccuracy, but this data is usually refreshed annually at best. And people change jobs, they move on, they get married, they have liquidity events, and a lot can happen in any given time or year. We've seen that at rapid scale since twenty twenty. And the result of all this when data is inaccurate or outdated is that this study from Deloitte that this is based on here showed that there's about twenty billion in waste every single year from misdirected targeting. And if you're running direct mail, that's gonna be a big part of that figure as well. So, looking at windfall, so we track net worth across more than one hundred million US households with over one hundred trillion wealth in our database and more than twenty million affluent households. We define an affluent household as having a net worth of over one million with an actual estimated figure, not a range. And we work with more than fifteen hundred organizations from nonprofits like Make A Wish and Memorial Sloan Kettering to commercial teams and luxury retail, travel and hospitality and financial services. And a common thread across all these industries is that everyone needs to know who their best prospects or customers are and reach them at the right moment. And that's really why we're here today. So from there, we'll jump right into direct mail and why this channel is in particular having such a strong performance moment today. So we'll jump into our next poll as we jump into this. So how are you currently using direct mail today? Just to get a sense of the types that you're running. Give just a moment here for a couple. Okay, it looks like we're getting a pretty good number of responses. I think we'll do about ten more seconds here. Great. We'll go ahead and share these results with you. Okay, here we go. So you can see there's a good mix, but a very strong presence of segmenting a subset of the database, and then a pretty decent amount who are doing the full database on a regular cadence, and some who are already in programmatic. So, and if you don't know what programmatic is, we'll tell you a bit about that as well, too. Great, so let's go ahead and move forward here. Wanna start by just talking about what the core types of direct mail are. So, the first is with prospecting. This is net new. So you go outside of your database to find net new prospects or leads, and the response rates are going to be lower here. And generally, you need more mail four to six times a year to see the kind of ROI. This is more a volume game. And then there's retargeting or with your database, working with people who you already know. These have a seventy percent higher likelihood to respond than a net new prospect. So your dollars go further here. But campaigns need to be more personalized because these people have context about your organization already. So you have a lot of opportunity to create really personalized and context driven materials here. And then the third piece is programmatic. And this is the evolution of both. So instead of planned campaigns, you're triggering mail based on an event, like someone moves, changes jobs, has a liquidity event, or has a certain level of engagement on your website. This is gonna be a lower volume play, but is gonna have significantly higher response rates and runs automatically once you get that set up. Today, we're focusing on the former, but if you want resources or a deep dive on programmatic, we have that too. You can reach out to us at questionswindfall, or reach one of us directly. I'm Kathleenwindfall, and JD you're JD at Windfall or JD at Windfall? We'll put our we'll put our emails in the chat to make that a little bit easier, but we'd be happy to do a deep dive. Just reach out and let us know about your interest in programmatic. So, why direct mail, and why direct mail now, especially in the world of digital everything? There's a lot of research on this that's only getting stronger over time. So, rates are considerably higher than digital. And even as overall volume has declined, the channel itself has gotten more effective because there's just less competition for the attention in a mailbox than in other channels. And there's a higher response rate, and also that ROI has proven to really make a huge dent. There's some other compelling facts that I think are really interesting. So, the first being, like the average person gets one hundred and fifty seven emails per day. I think I get more than that. I bet you all do too. And that's versus one to two pieces of direct mail per day as an average. So, most people feel overwhelmed by the number of emails that they get, but are gonna give more attention to that piece of mail that they get to their home. There's a sales boost when these programs are combined. So if you're able to mix your direct mail audience layered onto online campaigns that you're doing, you can make them all net more effective and get that lift that we're all after. And then interestingly, there's a seventeen day average shelf life for most of direct mail that's opened. So, if you think about that, then you've got a piece of mail that comes to your house and someone's not getting rid of it. They may see it on their counter. They may see it in their house for over a week or two weeks there. And that, I think, is something to just take a pause on for a moment, because that shelf life is huge. You think about email or other digital programs that are gonna be measured in seconds or minutes, if we're lucky. This is something where you're gonna get that brand recognition and impact that is gonna be stronger and longer. So, if we wanna dig into now why this works and how to make it work within the organization, that's how we'll start looking in, like what data, how do we get it started? So, at the starting point, direct mail has three core cost components, and understanding all three is gonna be important because where most organizations go wrong is optimizing one piece without thinking about the others. So, the first is in the packaging and the creative. What format you're sending, postcard, brochure, catalog? Is it personalized, glossy? How many versions? Is it a gift? These decisions all drive costs significantly. Then there's targeting. And this is the data layer. So, do you have the right address? Are you sending the multiple people address? Is it a current address? Have you run it through national change of address? This is often one of the most underfunded parts of the budget for these programs, and where a lot of the waste happens. So, that's something we'll dig into more today. And then postage, there's different levels and ways to send, and that impacts costs as well. And when you add all these things together, every single one of the decisions matters, but the one that has the most leverage on your results is going to be about reaching that right person as opposed to return mail or going to recycling and just getting that targeting right. And that's what the rest of what we're gonna talk about today is about. So, the reality that most organizations face is that direct mail is expensive, and you have to know who to pick. So, you might have, in this example, two hundred thousand people in your database. Maybe fifty thousand of them are genuinely qualified for the campaign, but your budget is for, say, ten thousand mailers. So, how do you pick those ten thousand? That decision is the difference between a campaign that's gonna pay for itself ten times over and one that's gonna just cost money. And some organizations are making this decision on gut feel or historical examples, and then we're going to show you how to do that with data. So, fact is that doing that is getting harder, not easier. Your ideal customer or donor isn't static. People move, change jobs, have those financial events that I mentioned that completely shift their capacity and propensity to engage with you. And without data that's refreshed constantly, your targeting is working off a version of someone that just frankly may not exist anymore. And that's one of the most important points we make today, is that this has to be up to date. So, your CRM data alone is not enough. Eighty four percent of organizations report that data decay is a big problem, leading to twenty to forty percent returned mail rate. And if you think about that financially, if, say, thirty percent of your mailers come back, you've wasted thirty percent of your direct mail budget before anyone read anything. And you only know what you can collect. So, your CRM tells you what someone did with your organization, but it doesn't really tell you about who they are, what they're worth, whether or not right now is the right moment to reach with them. And the question isn't just who's in your database, but who in your database is the right person to reach right now with that right message at that right address. And this is what the third party data component solves, And where the right data partner can change everything instead of targeting based on what you've collected, you're targeting based on this complete current picture of net worth, current event triggers in their world, and how likely they are to engage. And when you combine that with your CRM data, and layer AI on that, you're not guessing who to mail anymore. You're mailing with precision and with confidence. And with that, I'm gonna hand this over to JD, who's gonna show you exactly how this works in practice. We'll start with Windfall's direct mail capabilities and get into some more specific examples and stories. Thanks, Kathleen. So Kathleen just walked you through how direct mail works, but also why it's so hard to get. You're spending up to three dollars apiece, and you have data decay eating twenty, forty percent of your budget, and your CMO alone can't tell you who's actually worth mailing. That's exactly what Windfall was built to solve. At its core, we do direct mails as three things. First, we help you remarket the people that are already in your database who are most likely to respond. Not just the ones who gave last year or transacted last year, but the ones who have capacity and they have the household signals that say that they're ready now. Second, we help you reactivate lapsed contacts with the right messaging by understanding what's actually changed in their lives. And then third, this is a big one, we we go beyond your database entirely because if you're only mailing people you already know, you're already leaving money on the table. So whether you're sending offers, event invitations, catalogs, or even high touch gifts to your best prospects, the question is always the same. Who gets this piece? That's the question to answer, and it's not with gut feel, not with a ZIP code list, but with real household intelligence. So let me show you how this works. The windfall people graph covers a hundred million US household, and we update it weekly. When I say we solve the targeted problem, here's specifically what it means for your direct mail program. Number one, we flag bad households before you waste postage on them. It's is the person that you're addressing it to actually their office? Did they move six months ago? Are you mailing to a vacation home? We catch that. We show you exactly how that happened in the next slide. Number two, if you match to a prospect in the database, we actually have their correct primary address. So even if you have what you the address you have on file is outdated, you can still reach that household by using Windfall. You just leverage our address data with your existing direct mail vendor. Nothing changes in your workflow. You just stop getting returned mail. And number three, this is where it gets interesting for teams that wanna grow. You can actually find net new prospects that you never had in your database. Given that we track a hundred million households updated weekly, if you wanna go beyond retargeting and actually run acquisition campaigns, this is where we can help you by building those lists using the same intelligence that we use to score your existing database. And most of our customers start with number one, fixing what they already have, and that ROI alone usually pays for everything. So this is one of my favorite features to show because it's so simple, but the impact is so real. We have a trigger called primary address correct. What it does is it compares the address that you have on file in your CRM with a contact against what Windfall knows about households. If the address you have is incorrect or if it's incomplete, potentially it's outdated, they've moved, the trigger will return as true. So you look at this example. You have Jane Doe at one two three Main Street, but Windfall knows that the primary address is actually, you know, four five six Broad Street. The Main Street address is actually her vacation home. So if you mail her there, maybe she sees it in three months when she visits, or maybe it goes in the trash. Either way, you spent two, three dollars on a piece that didn't land. Now multiply that across your database, and you're starting to see a twenty to thirty percent, you know, true when that when that trigger is true, that's twenty three percent of your direct mail budget that's actually gone to the wrong address. And for a ten thousand piece campaign, two dollars a piece, that's four to six thousand dollars waste just from bad addresses. So this trigger, that's it's available to all customers. It's one of the things that we recommend first before running a campaign. You can think of this as a health check or data hygiene on your mailing list, and you know exactly how much room there is to improve before you spend a single dollar postage. So for database mining and targeting, and we need to get smarter, how do we help you find who the ideal candidate is before taking action? So you can imagine on the left hand side here, have a CRM database where all of the individuals are on a spectrum of likelihood to convert and also likelihood to respond, where convert could be whatever your your goal is from a campaign's perspective. Either it's investing, it's donating, purchasing, attending a webinar. We're really trying to identify all these green dots represent the ideal candidates to mail. They are qualified. They have financial ability to convert, and they're likely to respond. The red dots are folks that we know won't actually purchase or they won't respond. And everybody in gray, there's there's actually a lot of unknown, and we're trying to understand if they're actually green or they're actually red. By leveraging AI and data insights, we can actually sort and rank the entire database based on likelihood to respond and convert. So the green, the tops of the left, and then we stack rank the reds and the bottoms. This helps you by focusing your time and energy and budget on targeting the folks on the top left and then ignoring everybody from the bottom. Especially for a tactic as expensive as direct mail, it's really important to ensure that we're targeting the right people to kinda maximize that ROI. And by leveraging the machine learning and AI, we're spending our budget on a large majority of those in green They're the high quality prospects to really make that marketing dollar go further. So let's look a little deeper how that modeling could work for direct mail. So I wanna emphasize the the phrase on this slide, which is unique to your organization. This is not a one size fits all score that we apply to, you know, every database. The first step here is to really define who is ideal and who is nonideal. In other words, ideal would be individuals that we know to be those green individuals from that last slide, the type of profile that we wanna target with our direct mail campaigns. For example, the individual who have responded to one of your organization's previous direct mail campaigns. However, we also wanna identify the non ideal cohort. So this informs the model of, like, the characteristics of the individual that we do not wanna target. This ensures that we're not only optimizing for the ideals, but we're also then filtering out, you know, any types of, like, false positives. In other words, this will help inform the model to easily identify the households that just should not be mailed. Again, this would be the red dots on the prior slide. For example, the households that were mailed in the previous campaign that didn't respond. The model I'll put here would be a numerical score, and that would be based on the likelihood of the household is an ideal candidate to respond to your direct mail. And then putting this into practice, your organization could utilize a score to assess new prospects as they enter into the CRM. And by leveraging this model score, you have an early read or an indicator of whether every individual is most likely to be a green dot or a red dot. And then if they are a green dot, they're a gray candidate to be either be mailed or they're potentially, you know, a waste of marketing dollars. Lastly, our machine learning models are always learning, and as new data comes available, the scores can easily be updated to reflect most recent data. This includes updates to first and third party data, such as a recent email, website engagement, wealth updates. Models can also learn from recent mail campaigns. So that's a recent campaign result can also help influence the model. When thinking about targeting how do you leverage these AI models to improve it, even targeting over it also improves over targeting with heuristics. Here's a couple data points to use when thinking about segmentation. So first, let's look at two illustrative examples on the right hand side here. Imagine we have two prospects in our database, and we know that one is worth twelve million and the other is worth five. Depending on the specific product's price point or donation amount that we're optimizing for, both individuals could be qualified and considered ideal. We may also know some additional attributes about these individuals that could factor in a simple heuristics, or you think of it as qualification criteria. Have they recently engaged with the organization? They have boat owner, potentially a multi property owner? Based on these data points, both individuals seem like good candidates to be mailed as they're fairly qualified, but this is where the model score can help. The model score provides a clear picture of which candidates are best to mail. First, the models can pull up potentially hundreds of variables and factors significantly more than what we could pass it by looking at simple insights. Additionally, the model score can incorporate the likelihood to respond to direct mail, which may provide distinction when looking at two seemingly equal candidates. Alright. So as we think about how we go through this framework and how it directs to how it applies to direct mail campaigns and whether you're running once a year or once a week, how does the process typically work? So step one is really around determining your campaign goal. Are you trying to acquire net new? Are you trying to reactivate folks that may have lapsed? Are you trying to remarket to your best customers to increase their frequency? That goal really shapes how everything looks downstream, thinking through how do you build out the model, what type of creative or position you might use, and also how you might allocate budget. Step two is determining who are the ideal set of responders. This is where you combine what you know from your CRM along with what Windfall knows about the broader market. Here, you're going through and you're defining those screen tops. Next, leveraging AI to go beyond what potential humans can see. And this is really leveraging the propensity model we just talked about, how it finds patterns in hundreds of variables where the humans can process them manually. And you're now looking about this more from leveraging the model, leveraging the the machine learning and the computer data science to constantly recalculate, and it gets better and smarter over time. Part of this is also considering how you evaluate the model performance. So on our side, we'll run gain charts. We'll check the lift based on the holdout. Trust, verify. We need to really understand these metrics transparently because then you can then use this model, and it should really earn your confidence based on data, not based on promises. And the last step is really target measure and iterate. Launch the campaign, track the results through a ninety day attribution window, and then you feed that data back into the model for next time. This is the framework that works whether you're doing one person development or a fifty person marketing organization. Next, we'll run another poll. On this poll, how well has your direct mail program performed this year? We'll give folks a couple seconds. Let's pull. Share the results. We're actually seeing some varied responses across the board, both in terms of, like, it's underperformed to exceeded and a few a few that are not sure. So let's go through. Let's try to start parsing this through and trying to see how you can leverage Windfall if you aren't aren't already, how do you improve? So I was watching our quick stat. This is where people usually pay attention to it because when you pair the programmatic direct mail and also direct mail or direct the digital campaigns, you start to see, like, a sixty three percent increase win response rates. And you think of this as you have a targeted piece along with applying digital to provide halo effect. And we can think about why this makes sense. So someone can see your digital ad, and then a few days later, you know, physical mail piece shows up at their home or reverse. Have a mail piece, and they get retargeted and reinforces it online. You're surrounding that prospect in a way that feels much more intentional and not random. And then, really, this is, like, thinking about leveraging omnichannel marketing play. Direct mail on its own is powerful. You know, Kathleen showed you the stats. But when it's coordinated with digital, it's not just additive. It becomes like a force multiplier. And that's how when we think about hoarding the two, this really becomes possible by leveraging, like, timing and making sure that it's driven to be able to trigger based on some of the data. So if you're already running digital campaigns and direct mail, this really makes them both, like, work really well. Now let's discuss what implementation looks like. So step one is really identifying the goals of the direct mail campaign similar to what we discussed before. The first step is really around ultimate, what your goals are, how do you target to really accelerate either deal cycles, drive bookings, or provide air cover to other sales and marketing efforts. Next, going through diff confirming the campaign scope in creative. So customers here, they sometimes use our template to build out a campaign brief to help you get started. Based on your goals and your budget, you can determine the total mail size, duration, how often you wanna run the campaign. So as example, it could be a hundred mailed households daily for a six month campaign, and that's roughly, you know, eighteen to twenty thousand households that would be mailed. Then thinking about some of the rules for how you wanna prioritize. This could be based on a set of our model scores that you can use for prioritization. You can overlay on top of that additional set of heuristics. For example, you can ensure that, you know, there's you're filtering out or you're disqualifying anybody, let us prospect that you don't wanna mail. Either they've opted out to the email previously or they've been disqualified by the sales team as a bad lead, you can then filter them out and leverage the model score as a way to, like, determine which are the best households to include within this particular campaign. Next, it's configuring your data feeds. You think of this as, like, coordinating your lead source and then just working with your direct mail vendor to be able to pull in some of that data. You connect your CRM with Windfall. We have our integration or it could be an automated file feed. Windfall will set up those data feeds with your existing vendor, and then it's launching in measure. So launch the campaign, and as part of that ninety day attribution window, like, Windfall will provide those attribution reports to really understand the success and the ROI. Next, I'll turn it over to Kathleen to kinda talk through some featured customer examples. Great. Thanks, JD. And then after I share three examples for you, JD's gonna walk you through some great new features and functionality live as well. So let's jump in. The first one is an example of what JD just walked through. This is a luxury hospitality brand that had been mailing very small retargeting lists, people who already knew them, but they weren't seeing booking volume that they needed. So, they had already proven for them that NetNew worked on digital, and the question they had was, could direct mail do the same thing? And the first Net New campaign that they ran, it landed in homes in August of that year. And within like the first attribution window they had, they did have five new bookings and over a million dollars in booking revenue from that one campaign. And that's a great illustration about this ninety day window that JD mentioned. So they had a QR code in the mailer that was what they were relying the attribution on, so it was actually under counting. And then when they expanded into that ninety day attribution window, they actually surfaced that there was in progress additional bookings that hadn't closed yet, and that guests were starting to reference those mail pieces directly in their one on one booking calls. And that attribution window actually is what caused a lot of that ROI to be visible that may have been invisible without. Really good example of how those pieces have this long standing value and people are thinking about them later even a longer buying process, where they may not have that initial booking or initial action, but it impacts them later, and they reference it later. There's a cascading impact from these. And the next example, totally different. This may be close to home for some of you on the call who are a small but mighty marketing team. This example is Mercy School for Special Learning, a nonprofit who had a director of development that was the entire development team. And that's one person, no bandwidth for sophisticated analysis, and to not time to like individually research prospects, and not capacity to rebuild a strategy from scratch. So, when they thought about what kind of direct mail approach, they were originally doing what many do, which is pull the list of existing donors, mail them, and then hope that that brings results, which is logical, and it's a safe way to go, but it does leave money on the table. So, what they decided to change was something really simple. When they were pulling the list for the next campaign, they added high net worth individuals that had been flagged and people that the existing data wouldn't have surfaced. And they didn't overhaul the program. They didn't add more people to analyze, and they used the same kind of materials, and they trusted the data to help target which names to prioritize. And that campaign brought twenty thousand in net new donations, and these were from people that were never on their radar before. And so, this is just a smaller but perfect example of how you don't have to boil the ocean to get value and more value with better data. Sometimes it's something simple as adding the right names to your list that runs the program and makes the data do the work better for you. And then I have one more example for you, and this is a luxury travel industry customer, similar in profile to that first story I just shared, and the results tell an important story about how direct mail can convert over time. So, what you're looking at here on this chart in the x axis is days since a mailer went out, and what you see immediately is that there's this spike in the first few days. This is when the mail hits the home and people receive it. They're interested. There's people who are gonna act quickly. Those are your fast responders. But if you look at what happens after that, the conversions don't stop. They do keep coming day seven, day fourteen, and beyond, and that's the nature of, like, the considered purchase. And, like, if you think about a luxury trip, luxury travel, nobody's going to book that in the same afternoon that they, I guess not nobody, but many people are not going to book that in the same afternoon that they get a piece of mail, especially if they hadn't been considering it before. They're gonna put that on the counter, they're gonna talk to their partner, they're gonna come back to it. And that's where things like the seventeen day shelf life we talked about earlier come into mind, and you see it right here in the data. And the long tail conversions are exactly what that ninety day attribution window does and why it matters so much. So, this customer had measured at day ten and called it done, they would have captured a fraction of the attribution for the campaign. And the cumulative really tells that real story that direct mail doesn't convert in a moment, it converts over time in someone's home at the right moment for them. And then, our job as marketers is to get things to the right people at the right time. So they were able to run this campaign, have 10x the direct mail ROI, and run a pretty compelling program. And with that, I'd love to hand this back over to JD so we can see some real action here. Thanks, Kathleen. And don't forget, you can ask questions in the q and a, and we we're happy to cover those live. In terms of what I'm gonna demo, we recently released a update to all of our customers. So this is available to all the customers on the call. And then also within that, I'll show the capabilities for some of the newer folks that might be newer to TwinFall. We have a new feature that allows you to not only pinpoint, lookalike households if you're looking for net new, but there's also opportunities to go in and really start to size and understand more about these direct mail segments. If you give me a second, I can go through and actually walk you through. Alright. So what you're look what you're seeing right now is a sandbox account that we've built. This is for, you know, financial services. Within discovery, you have the opportunity to then create a segment. When you create a segment, we'll ask you how you would like to build it or, like, the ultimate purpose. Individual, you can think about this as you're really targeting people. So this works really well if you're building, let's say, a list or potentially thinking about it a digital standpoint. As you're thinking about building out direct mail, you wanna be efficient with mailing different pieces. You wanna send, you know, duplicate pieces of mail to the same household. So we have the ability to group by household. Next, we'll ask you, what would you like to do with it? Do you want to leverage the Windfall database to go acquire folks outside of your CRM, or you want to potentially do some retargeting, look at folks that potentially have lapsed? This is where you'd go through segment my data. For purposes of the demo, I will go through, like, net net new. And the next step is to really say, well, we've simply built out a model score for you. Do you wanna leverage this to help pick and choose who you're looking for in the model or for that particular campaign. For this purpose, I'm just gonna just go straight forward and continue forward here. This is the segment builder, which y'all known to use previously. With AI, you can say, let's go and identify households that have net worth of over, let's say, five million. You can also incorporate in, let's say, any other heuristics here. Let's let's look at folks that are not only, five million, but also have multiple properties. Since it's not new, we wanna actually acquire anybody that's, outside of our database, so let's go ahead and make sure we're suppressing everybody, including, you know, leads and prospects. And as it's it's building, you can get a general sense how large the segment is. So currently, it's two point three five million households that you can potentially target as part of the campaign. The other thing that I'll share is we actually not only helped with providing the ability to do household segments, we also incorporated a new heuristics. So if I go back, within here, we're actually available on all accounts. You can go through and look for direct mail responders. So what we've done is we've looked at our windfall data based on what we've observed, and this attribute actually identifies anybody that a household that has previously responded to direct mail. If you're going back to what, you know, Kathleen was talking about earlier in terms of trying to be much more efficient with your spend, thinking about how to target, you know, different individuals depending on your campaign goals. If you're going for, like, very precise and also thinking about, like, maybe getting some good quick wins initially to start with showing some of the performance or some showing some of the ROI to kind of build out that marketing budget, we'd recommend using the direct mail responders. You can start to see, like, previously, it was, you know, two point three million. It will affect the size, but the trade off here is, like, you get precision, and you know that these households responded previously to direct mail. So it it goes down to about nine thousand, but the other thing you can layer on top of it now is then thinking about, like, if there's specific geos or regions. You can start to play around with what makes sense for your program.