Track 4 – The Customer Data Goldmine Goes Way Beyond Credit Triggers

Credit and listing triggers are standard practice, but that's just the beginning. What other triggers and customer intel are the most relevant, best-ROI, and highest-conversion? How do you master and operationalize use of this customer data? Is it standalone toolsets or fully integrated tech throughout your funnel? This panel is about how you thread all of this from lead to close.

Transcription:

Joe Welu (00:06):

All right. How's everybody doing? Alright, Vegas baby. Okay, customer data goldmine, we're going to have a fairly quick conversation, not a lot of time to cover a topic that's pretty broad and we could probably talk for hours about, but we're going to try to give you some nuggets from the trenches. These gentlemen, I'm going to let us all introduce ourselves since we don't have a formal moderator. I am Joe Welu, CEO, and founder of Total Expert.

Shashank Shekhar (00:33):

I'm Shashank Shakher, Founder and CEO at InstaMortgage.

Joel Kehm (00:36):

And my name's Joel Kehm. I'm the CIO at Embrace Home Loan.

Joe Welu (00:39):

Alright guys, good to be with you guys. So we were talking last week guys, and one of the phrases that I believe is really resonates with me is that data is the new oil, right? Data's the most valuable resource in the world. And what I started thinking through over the last probably six to 18 months as we look at how do we unlock value in data, how do we take action on it? More importantly, how do we impact our businesses? And much like oil data in its own raw form doesn't do anything for you. And lots of companies are setting on rich repositories of data, but because it's not refined, it's not put into the right engine, you don't get any value out of it. Shashank, give us your purview on how you guys are thinking about data. Not just trigger leads and things like that, but the whole picture of the data arms race as we call it.

Shashank Shekhar (01:43):

Sure. The way we think about data and marketing to the data that we have is that I think we are moving from, and we have actually moved from stage where we used to do generic marketing to moving to personalized marketing, to hyper-personalized marketing. And that has happened is because there used to be information asymmetry between what we as lenders knew and what the bars knew. So earlier you could send generic marketing content to your database and they would still kind of receive it Well because there was an information asymmetry, which means you knew so much more than what your boards knew. So whatever information you provided was good enough. We have moved in the last 20 years, I think the biggest shift in communication has been the fact that those borrowers have moved from a stage of information asymmetry to information symmetry. By that what I mean is that they know so much more now. So your newsletter saying that, hey, you should be cleaning your gutter every six months and you should be pruning your grass every week, doesn't really cut well with them is because that's super Generic. Yeah, that's generic, right? It's like seriously anything else?

Joe Welu (02:48):

I stayed at a Holiday Inn Express last night too. What else you got?

Shashank Shekhar (02:52):

So that kind of generic market and most of the industry still does that, right? So hopefully some of us have moved from there to what I call personalized marketing, where you are going probably a little bit beyond that and probably you are customizing your content to maybe where they live, the zip code they live in, the town they live in, the city they live in, the state they live in. That's your personalized marketing. So instead of saying that, hey, you need to change your roof every 15 years. Now probably they live in Florida and they need to change it every five, but what really we need to get to the data goldmine that we are talking about, you really start leveraging it when you go to hyper-personalization, a model where you are giving them the information that's only pertinent and relevant to them. So not just, hey, you live in Florida and you should be changing your roof every five years, but your specific home because it has propensity to be impacted by hurricane or whatever, should be changing the roof this often or not just saying, Hey, you are refinanced eligible because the rates went down. That's generic marketing to hyper personalized saying in your specific case you qualify for 7.25% rate based on the information we have. So I think that's the roadmap that we as an industry need to take because the customer has moved beyond the generic newsletter and only then we'll be able to tap into the gold mine that we are here to talk about.

Joe Welu (04:09):

So I think what we've seen is the consumer has a different standard for how brands interact with them. And if you think about how all of us in the lending space spend a lot of time thinking about how do we increase lifetime value of a customer, how do we improve our conversion, how do we improve the cost to acquire a new customer? And one of the things that we ground on a lot as an industry is the importance of educating a consumer,

(04:40)

Communicating and educating that consumer, engaging with that consumer based on their individual human situation. And what I mean by that is, and what I think you're saying is ultimately if you're sending them things that don't provide any value, don't provide any context, their interest in engaging with you and actually finding you valuable as a professional or as a brand deteriorates. And so ultimately the way that you can really bring value to a consumer in that holistic journey is really understanding their point of view in life where they're at in that financial journey and then only delivering content, communication advice and product recommendations based on who they are and where they are. Agreed?

Joel Kehm (05:29):

Yeah, I can't agree with more with what you guys are saying. So I can use this as an example. At Embrace, a year ago we were predominantly direct mail that impersonal with the asymmetry direct mail marketing and it's just like, nope, taking advantage. And this is a theme that I've heard whole conference, we have an opportunity here, so it's really easy to get caught up in oh, the market's not good, it's an opportunity. You don't want to try some of this stuff when things are down. We went to a digital marketing strategy and the interesting thing about going to a pure digital marketing strategy, we actually get more information now than we did before this, whether we're driving them. So through their clicks, if it's coming off advertising, we know that they're looking to do home improvements, they're coming through, they're providing fully identifiable names and addresses, giving us their take on credit worthiness. It's way more than you ever had in the direct marketing world. And it begins to paint this picture.

Joe Welu (06:42):

Can I interject just one second and ask you to clarify something. I think this is important. When you say they're giving you information, my assumption, and I want you to correct me on this, is that you've actually built a certain level of trust up with your engagement with that consumer to get to a point where they're going to volunteer some of that additional data. Yes.

Joel Kehm (07:05):

Yeah, I'm going to say yes. And so even if you're using say rate table, some sort of long form lead generator too, there is a trust, they picked you off a list. There was something that resonated with them. They started down your journey where you begin describing it. They wound up there somehow because there was a message they saw in this really random generic world that we're calling the internet, they wound up in your stuff. So yes, to your point, something resonated with them.

Joe Welu (07:37):

Whether it might've been a piece of educational content on being a first time home buyer that ultimately gave them a familiarity with your brand that when it came time they're like, got it. Trust you, right?

Joel Kehm (07:50):

Yes. Then so we start to collect that. Now, one of the interesting challenges that's presented in the digital world mail is very serialized and everything came in one way. We sent a card out, got a call in digital, it's coming from anywhere. So we need to spend a lot of time to really create this data goldmine. It's almost like, alright, we're buying the mountain, we know there's gold here and strategically, so we're capturing and storing everything. We're not always a hundred percent positive what we're doing with it on day one, but we know there's something in here.

Joe Welu (08:26):

So if you think about the universe of customer data, both your own customer data, which is think of it as a gold mine that maybe you own, okay, that's raw. Maybe it's a bunch of oil wells that you haven't fully unlocked yet. I mean that's the way I think about it and you're not necessarily extracting the value yet from some of that data. And part of the reason is I think people and organizations, practitioners and organizations underestimate both the efforts and the length of time it takes to really pull the value out of that data. And what I mean by that specifically think about today how we're talking about, well, we've got credit triggers which are valuable in their various forms. That's a moment in time, but what's really more valuable is going way up funnel and starting to learn and understand what's important to that customer's world. Maybe a year, maybe 18 months out. But I'll ask you guys a question. How many people companies do you think have the foresight to run an 18 month journey on a set of customer or a set of leads?

Shashank Shekhar (09:48):

Let's talk 18 days. First we'll get to 18 months is because we are so biased at getting leads that converts ASAP, right? It's how the loan officers have been trained. It's how the management has been trained is that if this does not convert now it doesn't have that much value. And I think especially in a market like this where you need to nurture your leads so much longer. I mean you talked 18 months, who knows 24 months, 36 months given where the rates are given, where the inventory is and our industry is not set up to do that long form nurture is become long form nurturing is not. And that's where I think going from generic marketing to hyper-personalized marketing comes in is because if you don't do that, if you keep dripping on them saying that, hey, these are the five factors that impact the credit score. I mean I've seen that 24,000 times already is that these are the five.

Joe Welu (10:41):

Especially, I've bought five houses, I make 2 million a year, whatever investment bank.

Shashank Shekhar (10:47):

So it's the same email we send to everyone or same social media campaign that we do for everyone. And that's where I think the long form nurture is an art in itself and science of course on top of that and we need to get better in that is because in this kind of a market, it's nearly impossible If you were heavily biased towards the fact that I need to get lead today and convert it tomorrow, it's not a refinance market and even in a purchase market, it's a very long duration market and we need to focus on that.

Joe Welu (11:18):

You guys are both operators, which I come from the other side, I get the privilege of partnering with firms like yours and solving these problems. What do you think the difficulty is for most organizations at actually getting out of the gate and running an enterprise data strategy where at an enterprise level, I'm really taking command of the totality of all of the data that comes around my organization. And I mean this is a controversial, but loan databases have a lot of the individual originators. They don't want to part with that and they don't think you know what you're doing as a company necessarily, but yet we all would agree that the only way to really run a high value enterprise data strategy that's going to yield big results is to do it top down at an enterprise level.

Joel Kehm (12:16):

So backing up one step, you literally defined my job.

Joe Welu (12:22):

So you have no stress in your job.

Joel Kehm (12:23):

No, I have no stress in my job. So we talk about this gathering every piece of information and what sort of insights you're getting from, and I will tell you the early insights that you get the impatient, it's really clear what you don't have. There's some patterns we saw in how loans were getting originated and I'm like, I can tell just by the speed. So the number one thing, if you ever talk with me, if we ever go one-on-one is exactly is time sequencing when everything happens creates such a powerful picture. I'm like, I can tell you just because of the gaps here, they're holding onto a whole bunch of information we don't have this thing is too manicured to really proceed that quickly. So we know right then our enterprise data store has all of these upper funnel data points missing for an entire population. Now we have a couple different divisions and we take the exact opposite approach, which in a way is also problematic. We see what it could be. It's like again, it shines that light on what you need to do in order to create this stuff. But again, the other challenge this presents we were talking about turning around is the traditional sales model and full disclosure. So if I misspeak here, I'm a relatively new person in mortgage, I've been in it four years, I'm a supply chain guy, I'm very much.

Joe Welu (13:48):

You don't even know what you're getting into.

Joel Kehm (13:49):

Yeah,

Joe Welu (13:50):

Many people feel sorry for this guy.

Joel Kehm (13:51):

I know enough now. It's

Joe Welu (13:54):

Okay buddy. You're not alone.

Shashank Shekhar (13:55):

So happy for him that he doesn't know yet.

Joel Kehm (13:58):

So supply chain is all about systems of equations and impact make a change over here that impacts something way downstream.

Joe Welu (14:07):

This is sort of similar except with massive amounts of emotion.

Joel Kehm (14:11):

So there is no emotion in supply chain by the way, other than anger. But so we end up with this situation where the traditional selling models, there's a lot of assumptions about it starts with the realtor and we keep going back to this. We have this educated borrower out there, they're starting with their creditor, they are starting earlier and we have to engage with them and in many ways it creates opportunity, but it's a challenge. We've never worked that way before.

Shashank Shekhar (14:41):

So just to talk about enterprise level data management, so to say, as you pointed Joel, is that I think the challenge there is that the first of all, you can't trust loan officers do that is because they don't, Right?

(14:56)

I mean it's proven over and over again. You cannot have an 8% retention rate if your loan officers were that good in terms of staying on top of your customers. So that's one, let's understand that that does not happen if you're trying to run it at enterprise level. I think there's a lot of laziness that creeps into enterprise level data follow up and laziness is because it's not because technology doesn't exist. And just to plug total expert for example is that the campaigns already exist, the journeys already exist, but how lazy you are as the CMO or whosoever runs that thing is that how are you segmenting that you can't have your entire preapproval run through the same campaign for example or same journeys because even if you're lazy, let's at least segment into, okay, these are your FHA customers, these are your move up customers, these are your move down customers, these are your DPA, at least make 5, 6, 7 different journeys out of it.

Joe Welu (15:47):

As an example for a second. If you've got a jumbo borrower.

Shashank Shekhar (15:51):

Exactly.

Joe Welu (15:51):

Okay, all cash offer or 80% cash offer maybe is very different than an FHA borrower. Who is going to be getting in multiple offers on every property and the length of that journey for that particular borrower is going to be exponentially longer except there's a lot of organizations and we see it haven't necessarily adjusted that length of the customer journey. And so then they're looking at their attribution window of 18 months and they're like, well why did we have such a high percentage of borrowers that did not turn into transactions? And a lot of times it's because, hey, there was a lot of other factors and you just didn't keep engaging with them long enough.

Shashank Shekhar (16:37):

Not just keep engaging. You did not really evolve with the times. If your campaigns were created in 2021 for example, and you're using the same campaign, same content, same email marketing in 2023 when the market has literally turned upside down, then that why is it not appealing to the audience and not nurturing them the way that it should be?

Joe Welu (17:00):

So let's talk about the customer profile for a second and we think about the more data points you have on a particular lead or a particular customer profile around their financial picture. The more that you can engage with that customer, the more you can communicate with them with context and relevancy. Agree. So to do that properly data, the complexity of data is you've got zero party data, which is, hey, I'm asking my customer a question, what's important to you? I've got first party data, which is ultimately, hey, they did a transaction with you, their interest rate is X, their loan balance is Y, okay, I've got that first party data and then I've got this massive wide open ocean of third party data, some of which is valuable and it's the blending of all those three things that give you the comprehensive profile. How are you guys thinking about that as a strategy in terms of compiling those different components?

Shashank Shekhar (18:12):

Yeah, it's crazy how much of opportunity there is to embellish data with third party tools right now, whether you are buying leads right off the bat and then you can embellish with so much of from understanding what their social media profiles look like.

Joe Welu (18:31):

You're talking appending that.

Shashank Shekhar (18:32):

Appending through that. But even if you look at the data itself, the way we try to market the data is through, so let's look at say potential refinance opportunity in the next 6, 12, 18 months. Of course none of us in this room can predict for sure when the interest rates will go down and let's understanding what is the enterprise level effort to get those customers to come back to you for refinance. 80% of the lenders probably have no strategy. The other 20% at best, if I talk to them, they'll say our strategy is rate alert service is what we do. We send them rate alerts whenever the rates will go down, they'll come back to us for refinancing. That's literally using one data point out of probably 20, 30 different data points that you have. Only 50% of refinances happen because of lower interest rate.

(19:18)

The other 50% happens because of MI reduction because moving from arm to fixed moving from 30 to machine cashflow needs, taking cash out, cash taking HELOC, all of that, where does your rate alert service fit into all of that model of other refinance needs there is. So you don't even need to even go out and find third party resources to do it. So if your entire data strategy right now or if your entire strategy around let's get these refinance customers back to us when the interest rates go down is all about rate alerts, that's not going to work now. That's not going to work in the future. And that's where I think in instant mortgage we are doing a completely different approach and looking at data from multiple different angles in terms of what other refinance opportunities there might be. Even looking at age as a factor is that, are you getting to the 55, 56 where reverse might be an option.

Joe Welu (20:02):

Yeah, so we've talked about this and I think you guys are really forward thinking. You're saying it's not just about a rate alert or an equity scenario. It's about blending all of those attributes, all of those element, all those data points around that financial profile and then saying based on the combination of these things Rate, Equity, stage in life, hopefully we can get some life event data.

Shashank Shekhar (20:30):

Absolutely. And you can predict it.

Joe Welu (20:32):

That picture then gives you real context and it really allows you to connect that kind of human to human as we say.

Shashank Shekhar (20:39):

Yeah, life data is easy to predict. You had a child who was 16 years old when we closed the loan two years, we know that that child is going to college mostly.

Joel Kehm (20:48):

You beat me to it so I can use myself as an example here and this, I got two teenagers both college age I every day worry about cashflow.

Joe Welu (21:01):

I thought they were off the payroll.

Joel Kehm (21:03):

We went down towards some colleges in DC and they put the price tag in front of me and interestingly, institutional models now include the equity you have in your house, which if everybody knows on paper, we're all massively inflated and it was a little horrifying and so this is an easy one to see have any sort of demographic information on me. I got two kids, none of this is private. It's actually all public domain, really easy to find out what we have and here's the situation. Wow, college is a lot and there's going to be some things to look at. So that is absolutely building these profiles. The other thing I like to think of is patterns of behavior. There's different types of transactions that happen in someone's life. Usually it's very routine, but even then that's a baseline. It's like I would go to the doctor every year and get an EKG and you keep a baseline and then so we'd know the year that came and it didn't match the baseline. We start to get this, we get any sort of notification that might say, hey, somehow they're breaking out of their baseline pattern a little bit. There might be something here.

Joe Welu (22:15):

I really think it's from a lens of a believing that this isn't really, this is my words from talking to a lot of leaders, but would love your live feedback here. If you're going to exist in this industry and be successful, there's really not another option other than you are going to need to be data-driven or people like Rocket. If any of those guys are in the room, I think all of us would have respect for their strategy around data. They're going to find your customers if you're not. You guys agree?

Joel Kehm (22:48):

Absolutely what keeps coming up to me is data driven is not the same as I get 20 reports in my email in the morning data-driven is actively looking for things. It's really getting more analytical about what you have as opposed to just sort of this static presentation of reports, which frankly most people use those.

Joe Welu (23:15):

Dashboards. I got 150 dashboards.

Joel Kehm (23:18):

It's telling you what you know already.

Joe Welu (23:20):

But if my salesperson doesn't do the thing I need them to do, right?

Joel Kehm (23:23):

Yeah. It's interesting because anyone who's worked, I've in analytics for years and you get the ticket, I'm on the dashboard's not right. I'm like, well, and it's like, so we're telling you something already. What's the use of the dashboard?

Joe Welu (23:37):

We call that kind of, I tell our team, I don't need you to report the news to me. Okay, just read me the news. I like to look at the data and I think this is an amazing point and I think a lot of us can relate to this. Being data-driven as a company doesn't mean look at data and making reactive decisions and that's how most of our industry runs.

Shashank Shekhar (23:58):

That's what Credit Trigger does.

Joe Welu (23:59):

Yeah, that is reaction. Being data-driven is having a comprehensive strategy around the customer journey from customer acquisition to customer for life and how do I actually create opportunity and loyalty with my customers using data as a strategy and all the way into the execution, right?

Shashank Shekhar (24:23):

Yeah, I think predictive analytics, that's why it's so important is you can predict behavior. There is, I mean literally decades of data that you have, which is by which you can figure out and I understand that. Look, I mean I get it that we were not so data driven in the last decade or so because the last decade was kind of relatively easy for the industry. We had our ups and downs, but if you look at from 2010, 2011 till about 2021, we had a great run as an industry last we had a solid decade of low interest rates.

Joe Welu (24:55):

Good thing everybody saved their money.

Shashank Shekhar (24:56):

Yeah, exactly. I mean is a full decade. But now I mean with the rates where they are, the inventory where and it'll stay. This is not a short-term industry situation. We are in, I was talking at last evening's panel, I was saying exactly the same thing, that we need to stop pinning our hopes on the fact that the interest rate will go down and it start finally grow up and become an adult and figure out a way of living in an interest rate environment like this. And what does that mean? That means getting every ounce out of the data and the database that we have already collected over the last decade or so. And that's why data intelligence and data, predictive analytics and actually working on that, not just having dashboards is important is because moving forward, getting every single client is not going to be easy. It's not going to be what the last decade was. So we need to work on that.

Joe Welu (25:45):

Last point I want to get feedback on then we're going to have about seven, eight minutes for questions here. How do you guys think about getting, so you can have an enterprise data strategy at a corporate level, corporate marketing teams, I've got a digital team, a marketing team, a data team, sales team. But if it's actually not integrated into the sales and marketing process, meaning all the way through to the actioning of the salespeople and utilizing those instances, how do you guys think about that in terms of importance, not just the systems to get insights and some people in terms of their maturity, they maybe just want to start working on hey, I need to segment my database into basic cohorts as step one. Okay, that's fine wherever you're at in the maturity, but regardless of that, none of it really matters unless you're influencing how that last mile happens. What's the thing sent to the customer? Is my sales person doing with it? How are you guys thinking about that connection between the intelligence and the action in your sort of customer journey?

Joel Kehm (26:54):

Yeah, That's a great point and I kind wanted to bring it up great place to tie things together. So we really think about our data when we look to make it actionable at a process level. So it should streamline and I will approach everything as we're coming in. What do we know at this point? Because at each, if you think it's sort of like a flow chart with probability decisions rather than yeses and nos and what do we know here? And you're augmenting your process one way or the other depending on what you learn because you begin to learn over time as things are coming in, you can then wait it with some of the other information you've gathered. And again, so it's not a, I look at this report, this is what I do. Oh it's this kind of borrower, this is what I do. We need to get more specific than this kind of borrower, this person, they seem to be going like this is how we need to react. But it's an enabler to a process flow. So it's part of this entire transaction we're talking about from the 18 month cycle from lead all the way through a closed loan.

Joe Welu (28:04):

You got to get the whole organization around this mentality, right? Yes.

Joel Kehm (28:09):

Yeah. The entire organization, everybody, there's definitely this desire to just focus on your little world, but what we're sending in, what the upstream is sending into you is going to be different depending on it. You sort of need to everyone on board with this general flow and understanding, oh, this is how we're going to work. It's a giant, you're choreographing this dance, this conversation between the borrower and your business as an entity. And I like to use the description as a borrower as a conversation when we're like, what data do we move? Do you get annoyed when someone asks you the same thing twice? Do you get annoyed when someone asks you? That happens over and over again. If you think about our loan process today where you're filling out forms, then you get a phone call and they ask you the same thing you just filled out and that is part of choreographing this. So it is now this seamless, whether it's tech or whether it's a person you were just continuing with the other piece left off.

Shashank Shekhar (29:12):

Yeah, I think the last mile problem in the industry is acute is because you can do all of the facts that we mentioned here on the panel, but at the end of the day it's the loan officer who's picking up that phone and having that conversation or sending that email and closing that transaction. And that's where I think of course everything that Joel mentioned is relevant that you get the entire organization behind you but also need to be realistic is that that does not happen. It's very hard to drive that down through loan officers every day. And that's why you need to have a flexible model where you have, and that's why it needs to integrate into your process flow where, hey, if the lead comes to a loan officer doesn't follow on day one and she doesn't follow on day two, then what happens on day three?

(29:57)

Do you have a dedicated team to follow up on those leads and then maybe pass that back onto loan officer or have a differential pricing or differential compensation on those leads so that you as corporate don't lose out on that deal? The loan officer still gets to keep the ownership of the data while you are still not losing out the opportunity on doing that. And that's something which is not generic, it's not as if I can tell you that what we do at Intra Mortgage will work with every single organization, but I know enough loan officers that that's how the model works is that you can feed them all those leads and trust them to nurture those leads and that does not happen. That's why differentiate model. I mean every probably work better.

Joe Welu (30:35):

Sorry to interrupt but no, Think about the fact so many independent banks and lenders, mortgage banks out there right now are struggling to get that last mile of production, right? Well that last bit of production every month, every bit matters, but yet don't you think there's a lot of business because there's not a unified data strategy for a lot of companies, there's piles of business that still hits the trash can because they don't want to rock the boat with an originator. They know the originator's not doing it, but it's their, so it's like you got to take a stance at an enterprise level and decide.

Shashank Shekhar (31:14):

Yeah, That's what I'm saying is that you need to come up with a policy that's flexible enough where you are not of course alienating your loan officers, but at the end of the day you're also not losing out on the opportunity. And that's a discussion that might be different for different companies and I'm not here to tell you what works for us, but you need to be flexible with that.

Joe Welu (31:31):

Well, and ultimately I think there's a blend of both that can work because if you're saying, look, this is why and it's adding value to you and it's an extra deal or two a month for you if you subscribe to kind of our approach, once they get deal flow, then the conversation changes.

Shashank Shekhar (31:47):

But most of the new things, that's how we run internally is that we try to show proof of concept saying, look, this is the model we came with. We run it with say two, three or four loan officers, whatever, show that this thing actually works and then take it to the full team saying that it's because otherwise most loan officers will come back and will kind of protest and say this is something that we don't want to do. But once you have proof of concept, it's so much easier to drive adoption for any new policy and model that you have versus just come up with one and try to blanket it to the entire team. And that's the approach that we have taken with anything new that bring on in the team.

Joe Welu (32:22):

Great stuff. Alright guys, we've got time for probably two questions, maybe three. Go ahead.

Audience Member 1 (32:26):

Alright, cordless media, great discussion. General, would you agree that the holy grail of data really is to send the right message to the right person at the right time and that's really what we're talking about. Are you familiar with the trade desk? The Trade desk is a demand side platform where they actually have about 70% of the market. They advertise that and they ingest first buying data that you can action home buyers, homeowners, trade desk agents, the trade desk, check it out, check it out. But it's really important. The reason I bring it up is that we just got integrated directly with the trade desk. We are now with data. We have the largest advertising platform for all buyers.

Joe Welu (33:21):

Fantastic. Well I'm sure a lot of us will check it out. Thank you. Other questions?

Audience Member 2 (33:34):

I'm krill. If credit triggers are no good, what triggers are good?

Joe Welu (33:39):

So think about it beyond triggers, and this is my response, okay, I'll let these guys go. Life event data. I want to know Divorce, Marriage, Death. I want to know major life milestones that are coming up. I can get those insights. There's lots of providers out there. Our company is investing a lot in this space right now and many others Are as well. But the key Is to find out What do you need for your current approach? And in terms of the systems, do you have data pipelines built? If not, it depends a little bit on your sophistication, but we believe a combination of those. Hey, I put my home on the market, here's my equity balance, here's my interest rates, sort of the basic things about the financial profile. But then I need life event data that both my customers, there's ways to get that information in a survey type form as part of your process within your customer journeys. And then there's also third party sources and it's blending the two. So it's really thinking about a trigger as a lower conversion percentage, but it's at the moment in time where it is right there. So it very short attribution window, the life event data much more valuable but longer attribution window. Does that make sense?

Shashank Shekhar (35:00):

So, here's our company's philosophy on trigger is that we should create the alert that causes the trigger and not the other way around. So because when you are getting to credit trigger, it's after the fact, right? It's basically somebody has already pulled that trigger, now you're reacting on that and now you're,.

Joe Welu (35:20):

Your last line of defence.

Shashank Shekhar (35:21):

It's your last line of defense. I mean there's nothing wrong with that. It's better to have at least the last line of defense than have no defense at all. So nothing wrong with credit triggers at all, but you should ideally be in a position where your alert is causing the trigger, meaning you are the one who's predicting that you are potentially refinancing, potentially putting that home for sale. And then you are way ahead of the game in that way than doing the credit trigger. So again, nothing wrong with these triggers at all. Our discussion is more about you can do so much more than be behind the curve all the time in trying to play the last line of defense.

Joe Welu (35:54):

The way we think about it is layers of value. My base layer, I want my last lines of defenses in place and then each of my layers, I want to go up funnel as far as I can and I want to layer as many of that, as much of that relevant contextual data about life milestones, what's going on in your financial journey, and be bringing all of that in as much as I can.

Joel Kehm (36:16):

Yeah, I've got to agree with these guys. I've got plenty firsthand knowledge, especially this last year. We've used industry used credit triggers forever, but they just stop converting. We have better luck when we're farther up the funnel. When we engage, we build a relationship early and that was just doing the math. That one was, hey, this one is converting and is a lot cheaper. This one's expensive, doesn't convert at all. It was kind of an easy decision there. But this concept around gathering these insights over time, and so I will use the expression, I used it earlier looking for patterns, patterns of life events are patterns. You see this 20 times and then you have this new prospect and you start to see this same pattern emerge. You know what, there's probably fire where there's that smoke. Absolutely.

Joe Welu (37:16):

Guys, we are out of time unfortunately. Thank you so much. I'm sure all of us would be happy to jam and have a conversation outside. Thank you. Thanks.