Secrets to Scaling SaaS Businesses With Patrick Hannon
- Quik! News Team
- 3 days ago
- 32 min read
Patrick Hannon is the Vice President of SaaS Commercialization at Fidelity Labs, where he develops and implements go-to-market strategies for innovative financial technology solutions. With over 12 years in the WealthTech industry, he has held significant roles at companies such as Nitrogen (formerly Riskalyze) and Advicent (now part of InvestCloud), gaining deep expertise in enterprise wealth management. Patrick is recognized for his commitment to mentorship, actively guiding young professionals in their career development. He is also a thought leader, focusing on integrating technology into financial planning and compliance to enhance advisor workflows and client experiences.
Here’s a glimpse of what you’ll learn:
[2:25] Patrick Hannon discusses Fidelity Labs and how it helps advisors and consumers with financial tech
[4:17] Leveraging Fidelity's internal resources to build enterprise-grade startups
[6:20] How Fidelity Labs differs from other incubators and custodians
[7:56] Opportunities for external entrepreneurs to collaborate with Fidelity Labs
[10:16] Criteria used to evaluate new business ideas and technologies
[12:31] Patrick explains how Fidelity leverages proprietary data to build competitive AI solutions
[15:16] Real challenges AI is solving for financial advisors today
[22:07] How Catchlight helps advisors with lead scoring and personalized outreach
In this episode…
Navigating innovation in the financial services industry can feel like a tightrope walk — balancing compliance, customer trust, and speed-to-market. Many fintech startups struggle to become enterprise-ready while serving advisors effectively and efficiently. How can emerging technologies gain traction in a highly regulated space while solving real problems for advisors and clients?
Patrick Hannon, a SaaS commercialization expert with over a decade in WealthTech, offers a blueprint for aligning startup agility with enterprise-grade expectations. He shares how leveraging proprietary data and maintaining a customer-centric mindset leads to stronger, more scalable solutions. By melding AI with the power of human relationships in advisor-client interactions, startups can solve compliance, marketing, and operational pain points. Patrick also dives into the importance of giving new talent room to fail safely as a method of cultivating leadership and innovation.
In this episode of The Customer Wins, Rich Walker interviews Patrick Hannon, VP of SaaS Commercialization at Fidelity Labs, about accelerating innovation in financial services. Patrick explores how AI-driven solutions help solve real-world advisor challenges, discusses startup incubation, the value of proprietary data, and balancing speed with regulatory compliance.
Resources Mentioned in this episode
"The Art of Effortless Wealth Recovery With Mike Betz" on The Customer Wins
"Unlocking Capital Efficiency for Advisors With Andrew J. Evans" on The Customer Wins
"Transforming Advisor Engagement Through AI With Spenser Segal" on The Customer Wins
"The Six F Strategy for Success With Brett Gilliland" on The Customer Wins
"[AI Series] Streamlining Operations With Cutting-Edge AI Technology With Babu Sivadasan" on The Customer Wins
Quotable Moments:
"The challenge of corporate innovation is blending resources and speed, but it’s a wonderful challenge we have at Labs."
"Fidelity Labs isn’t just for Fidelity customers; we provide technology to any advisor, regardless of where they custody."
"Most AI solutions are still looking for a problem to solve, while advisors just want efficiency in compliance and workflows."
"Everyone should be AI-driven, but be cautious when it's client-facing or includes client data."
"Think about ways to send the elevator back down and give someone a shot to grow or fail safely."
Action Steps:
Build enterprise-ready systems from day one: Starting with strong compliance, legal, and data foundations enables faster scaling and long-term sustainability.
Focus on solving real advisor problems with AI: Applying AI to practical friction points like compliance or note-taking drives meaningful adoption and value.
Leverage proprietary data for competitive advantage: Unique datasets fuel better AI models and create a defensible edge in the marketplace.
Test new ideas in safe environments: Giving team members stretch goals in low-risk scenarios encourages innovation and builds future leaders.
Use scoring models to prioritize leads and client engagement: Lead propensity scoring helps target the most promising prospects and boost conversion rates.
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Episode Transcript:
Intro: 00:02
Welcome to The Customer Wins podcast, where business leaders discuss their secrets and techniques for helping their customers succeed and in turn, grow their business.
Richard Walker: 00:16
Hi, I'm Rich Walker, the host of The Customer Wins, where I talk to business leaders about how they help their customers win and how their focus on customer experience leads to growth. Some of our past innovative guests have included Mike Betz of 11thEstate, Andrew J. Evans of Rossby Financial, Spenser Segal of ActiFi. And Brett Gilliland of Visionary Wealth Advisors. Today, I'm speaking with Patrick Hannon, VP of SaaS commercialization at Fidelity Labs, and today's episode is brought to you by Quick, the leader in enterprise forms processing.
When your business relies upon processing forms, don't waste your team's valuable time manually reviewing the forms. Instead, get quick. Using our Form Xtract API, simply submit your completed forms and get back clean, context-rich data that reduces manual reviews to only one out of a thousand submissions. Visit Quickforms.com to get started. Now, before introducing today's guest, I want to give a big thank you to Michael Partnow of JiffyAI for introducing me to Patrick.
Go check out Jiffy's website at jiffyAI and check out my interview with Babu Sivadasan, the founder of JIFFY, to learn how they help financial service companies streamline their business. All right. I've been looking forward to this conversation for a while now. Patrick Hannon has over 12 years of experience in the wealthtech industry, where he has helped SaaS companies scale, grow and innovate. He's currently the VP of SaaS commercialization at Fidelity Labs, the innovation arm of Fidelity Investments, where he leverages his deep knowledge of the financial advisor market and the SaaS business model to create and execute go-to-market strategies, foster sales and partnerships, and drive revenue and profitability for partner firms.
And in his free time he likes to travel with his wife and kids, play pickleball, and celebrates tequila Friday. Patrick, welcome to The Customer Wins.
Patrick Hannon: 02:06
Rich, thanks for having me.
Richard Walker: 02:08
Oh my pleasure. So for those who haven't heard this podcast before, I talk with business leaders about what they're doing to help their customers win, how they build and deliver a great customer experience, and the challenges to growing their own company. Patrick, I want to understand your business a bit better here. How does your company help people?
Patrick Hannon: 02:25
Yeah, well, that's a that's a big question. But for me specifically, I'm working at Fidelity Labs. So we're a business unit within Fidelity. And we're focused on creating new technology for advisors and consumers to use that will enable them to further their financial lives. Fidelity Labs currently has seven businesses in its portfolio.
We've got a few in stealth mode. We've got a few more ideas on the way, but really, we it's not just one business and labs, but we've got an incubator where we're creating new software businesses to support financial advisors and consumers.
Richard Walker: 02:57
That is really cool. So incubator makes me think these are startups, that they're really young. What are the various stages these companies are in at this point?
Patrick Hannon: 03:05
Yeah, we have all stages. So we have an idea board that we're looking at. We have probably two businesses that I would, I would call very early incubation in which I get to help these businesses go to market and maybe find beta testers. And that could be financial advisors or firms that want to test this new technology. And then we have a handful that are out in the public sphere that are serving clients and really are enterprise grade.
Today. I think some of the key components that we're thinking about within labs when we start a business is can we solve a fidelity problem or challenge first? And so one of our most mature businesses is called cipher really bleeding edge at compliance solutions. But those solutions are enterprise-grade out of the gates because we're looking internally first and saying, can we solve a challenge that we see within our ecosystem as our first customer?
Richard Walker: 03:57
Man. I mean, look, being a fintech wealth management tech firm myself, it is so hard to get to that point where you are enterprise-grade. I mean, it took me like, what is it, 20 years to get Soc2 audit finally.
Patrick Hannon: 04:09
Yeah.
Richard Walker: 04:10
So it sounds like you have this amazing advantage for the companies in your portfolio to get there. How do you help them get to enterprise grade so quickly?
Patrick Hannon: 04:17
This is the blessing and curse of what I'd call corporate innovation. Right. And so the dream and what got me really excited about working at labs, you know, from my previous role at Riskalyze, which was wonderful in different ways, but was this idea of, hey, you've got Fidelity's resources and expertise and you just this business unit that wants to innovate there. And I think at its best, it's what we're talking about here, which is you have so many resources to say from a compliance perspective, from a legal perspective, from a data perspective, can we get some help in building these ideas and execution? And at the same time, it's the challenge of corporate innovation is moving as fast and as nimble as a startup like Quik or Risk or Nitrogen or others, but it's finding that blend of resources and speed, and that's the that's the wonderful challenge that we have at labs.
Richard Walker: 05:12
Okay. So I think it took us three, maybe six months to go through the due diligence process with Fidelity. Do you guys get to shortcut that internally?
Patrick Hannon: 05:22
No, no we don't. I can tell you we don't. Fidelity has I was at a I was at a conference where we were presenting, and I sat in on a session on how to stand up AI model governance. And, you know, Fidelity has had that for 6 or 8 years now on, on governance specific to AI models and maybe longer, but it's something that we have a robust policies and procedures, and we don't get to skip the line on that. Everything goes through Fidelity's ecosystem for that.
And Fidelity Touch is probably 1 in 6 American households. And so from a data and privacy standpoint, it's world-class.
Richard Walker: 06:03
No. That's amazing.
Patrick Hannon: 06:04
Which when you're in a startup can be frustrating because it's it takes time.
Richard Walker: 06:07
Yeah. So like, I don't know that I've met anybody like you or a labs type of division like this. And I'm sure there's a few out there. Do you know of others that are you're competing with or do you even think about that?
Patrick Hannon: 06:20
Yeah. I think what makes Labs Fidelity Labs unique is that compared to other incubators, specifically in the wealth tech segment, Fidelity will provide its technology to any advisor, regardless of the custodian that they keep. And so Fidelity Labs isn't looking just to provide technology only to Fidelity customers. If we work with a Schwab shop the primarily custody is at Schwab. The Fidelity Labs businesses are not.
They don't care who they're working with. They just want to serve advisors. And so we don't share information about clients, you know, in other parts of internal Fidelity. Our customers are our customers. We keep that very centric to who we are.
But yeah, we get to work, work with others. There are others at some other custodians, but they usually keep their technology in-house. And so they're looking to incubate and deploy in-house and not to the public in the same way that Fidelity is.
Richard Walker: 07:16
So I presume that a customer is actually engaging with safer as an entity, not Fidelity Labs as an entity.
Patrick Hannon: 07:22
Correct. The entity could be safer. It could be Catchlight. It could be Fidelity stock Transfer. It could be Fidelity Private shares.
We've got a few others that I just can't quite talk about yet.
Richard Walker: 07:34
Yeah. No. Of course. Keep the things under wraps that you need to. And I understand that oh so well because if it's not proven, it's not in the market yet.
Yeah. Okay. So, look, I'm an entrepreneur at heart. I've started ten companies since age 12. And this makes me think, oh, how exciting.
I have this great idea that I wish Fidelity would fund and run with you take on outsiders? Or is it only inside?
Patrick Hannon: 07:56
Yeah, we love ideas and we get some of our best ideas from the customers we ultimately serve. It's not just challenges that that we see internally. We also hire entrepreneurs and residents. So rich, if you ever want to give up this shtick here quick, you're welcome. You're welcome to take the corporate life with us.
But what's really fun about that is I've had some really interesting conversations in the last few months of folks who are ready to work and to build within our ecosystem. And to give you an example, I'm on a team that has what I call like three legs of a stool. One person is very much focused on what are the new ideas? How do we build business plans, business cases, business projections for those this person is responsible for sizing, markets, etc. but like very early stage bleeding edge get ideas out to the market. I'm the second leg of that stool, which is to think about how do we commercialize these, like, are these viable businesses?
What Tam do we set up first? Where do we point sales reps at? How do we structure and support sales teams so that they're successful? And then we have a third leg of that stool, which is sales operations. And this is a person.
His name is Aidan, but he makes the world work. It's just and you probably have your Salesforce admin. Aidan's got a team of 12 that support all of the businesses. What's really nice for like an entrepreneur in residence or someone who has an idea is they come in and a lot of the systems are just set up where they can walk in, build the idea and take it to market in a way that we don't. You just have to do all that yourself in a way that you're probably wearing a couple of hats.
Richard Walker: 09:36
Yeah, it sounds like you bring a level of maturity to a startup that they don't have normally process systems. You know, like you talked about the compliance data, all that architecture. So really the entrepreneur gets to focus on the idea more than anything else, right?
Patrick Hannon: 09:53
Right. This is where we get we get to apply speed in certain areas that you just can't find externally.
Richard Walker: 09:59
That is amazing. All right. So you mentioned one of the criteria is that you really want to build a business that can serve enterprise-grade at Fidelity. Right. What are the other metrics that you look at to say should we do this or not.
I mean, would you ever take on a business that could only serve Fidelity?
Patrick Hannon: 10:16
Certainly, I think our business unit is optimized to saying it should it should be wider than just Fidelity. I think if we're solving Fidelity problems, we say, hey, if Fidelity has this challenge, there's other businesses that have this challenge as well. Enterprise grade. I think safer is a great example of this. Safer is one of the ideas or businesses that got me really excited about moving to Fidelity and Fidelity Labs, and they took 30 years of, of ad review data that Fidelity had.
Right. So Fidelity had ad review just like anyone else. and they took 30 years of data and said, how do we build a large language model to support advisors so that they don't have to go through a two-week compliance, you know, loop, right. So you generate content. You got this great blog post.
Let's just have an AI system review that for all the compliance concerns, so that we as the advisor can have speed to market. And so to answer your question, when I think about enterprise grade, it's solving that that critical thing where we said, hey, we can we can streamline our compliance systems internally so that we can be serving our advisors faster. But we can also give this technology and sell this technology to other compliance departments. And it's been a huge win. And compliance isn't always the sexiest place to start.
But the fines are crazy for firms who don't get it right. And the advisors just want to serve clients. Like, let's just make it easy for them.
Richard Walker: 11:38
You know, my mentor told me when I said I wanted to start a business and I didn't have an idea. He said, look, find something that is boring. Nobody wants to do it. Everybody has to do it repetitively and make it better, which is forms for me, right? I mean, nobody wants to fill out forms, so it sounds like that's a great application.
But there's something else in what you're saying, which is the application of AI. And one of the things I have been learning about AI is that there is sort of a level playing field in the sense that anybody who has access to ChatGPT or Claude or these other models, what's going to make you unique? And I'm talking like 2 or 3 years down the road when these models have matured a bit. What is going to make you unique? And you just said it 30 years of ad review.
That is what you guys brought to the table and applied it with AI. Are you seeing more opportunities like that? And you don't have to tell me exactly, but is that one of the ethos of how you guys look at things?
Patrick Hannon: 12:31
That's absolutely how we look at things. For Catchlight, it was looking at predicting client conversions, which we can see in a way from a data set that other businesses simply can't. And you can take the best engineers at MIT and they if they don't have access to the same data, they can't train the algorithms in the same way. And so that is absolutely a competitive advantage that these businesses have. AI is really interesting to me in our space.
I want to get on a soapbox here for a second. Rich. It's interesting to me in the sense that for most financial advisors, I feel like everybody has slapped ChatGPT or Claude on to their systems. And they said, hey, this is the solution to all your problems, and you have advisors. And what the average advisor is like 50 technologies that they use in a day to serve clients.
Now the average hold time for technology is like 14 years. The average advisor is flipping over less than 5% of their tech stack a year. And so you have all of this commotion and noise around AI for financial advisors. And the reality is that most AI solutions are still looking for a problem to solve. And most advisors are simply sitting, going, I need I need to be more efficient in compliance and workflows.
I need better marketing, and I just want someone to take notes for me. And so when you look at AI in our space, it's why the note takers are taking off, because it's solving a real challenge. Marketing continues to be a real challenge, and back office and compliance continue to be a real challenge. And like those are areas that you can actually look for AI to solve problems in a in a space where ChatGPT and Claude, like, they're not compliant for financial advisors and they're still looking for a problem to solve.
Richard Walker: 14:13
That is such an important point. And look, I'm going to use my own history as an example here because when we came out with Form Extract, so I recall ChatGPT came out in 22 like late November, early December. Right. And so I was just enamored with it from that point forward. And so I went into 23 saying like, first things first, we're going to use AI in our company.
I want everybody to learn something, find a tool, make your life better, whatever. We're going to build a product that's driven by AI. And when I sat down to brainstorm about what product to deliver, I started with that mindset You just said of like, oh, I could apply it here. Wait, what? I need a problem.
And I kept coming back to like, well, that's not a good problem. That's not a good problem. And then I finally said, what are my customers asking for? And this was the breakthrough, Patrick. Everybody for 20 years has been saying the same thing to me in one way or another, which is now that I can get the form filled out and get the data on the form, how do I get the data off the form?
Yeah. And the advent of AI and that question totally sparked a new direction. So we invented form extract.
Patrick Hannon: 15:16
And what I love about that example is that the problem has nothing to do with AI. AI is the solution to making someone more efficient. And it's like that's the example that I look at the fintech map, or I look at a life cycle of an account. You know, that an advisor might work on. Right.
You have data in before they're engaged. At some point, you engage with them, they become a client, they sign up, you've got ongoing monitoring review. Maybe you rebalance performance reporting and billing client portal, and you've got a compliance oversight like that is the spectrum of where an advisor will engage with data as it relates to an account for a consumer. Like that's the life cycle there. One of the things that gives me most hope in our space is just the ability for better integrations, because I can just build the integration in a way that used to take dev teams weeks or months to build an integration between systems like API endpoints.
Like we're becoming more mature. You look at the Kitces fintech map from like I'm going to call it 2000 to 2015, like 15 years of like feature development for all of these different businesses. And they were never looking externally on how to integrate with each other. And since 2015, like, what do you need in a financial planning application anymore, rich? Like, we've got rolling graphs.
We've got like we're so deep in the weeds on features. What we need now is all these applications to talk in like very seamless ways to each other. AI is going to play a huge role in developing that technology to allow dev teams to go faster. Is it going to like, change the way that the advisor talks to the client? Hell no.
Like it's just not like this remains a human-to-human business. But I will be on the periphery enabling systems to move faster.
Richard Walker: 17:00
Yeah, I do think it's about enablement. And I think at the conference where you and I were at, somebody had mentioned that they were building APIs on the fly with AI. Yeah. Like a user could just say, I need this and boom, it would build the API on the fly. That blew my mind.
I'm like, that's a whole new way of thinking about APIs. And when you when you talk about getting data to move from place to place and system to talk to different systems, that is a fascinating concept. Yeah. I'm going to throw an idea at you because I think the world's going to go here. I haven't heard anybody say this yet.
And this is my million dollar idea. Maybe I'm giving it away. I shouldn't, but I think we're going to have agent AIS that work on behalf of us. So I'll give you an example. My wife is a case manager at a hospital, so she has to then schedule pickup for a patient.
Maybe oxygen tanks will be delivered to their house. Maybe going to a skilled nursing facility. Whatever. She has to call and call and call and call. Wait for them to answer the phone.
Wait for them to give her the answer. Do they take Medicare, etc.? What if what if she had an agent, an AI agent that could just call that company's AI agent and do that conversation for each other? So I think we're going to have an advent of different people having different types of agents, just communicating with each other to perform transactional type of queries and services, sort of like an API, but more freeform. What do you think of that idea?
Patrick Hannon: 18:19
I'm skeptical. I really am. And I think specific to our industry, if it happens in our industry, which it certainly could if the technologies available, if I happen, I think we will be. When you look at all the industries, we will be the most the latest on the adoption curve.
Richard Walker: 18:37
That's always been true. Oh my gosh.
Patrick Hannon: 18:39
I think oh I love the idea of this rich in concept. I think compliance is going to is going to slow that down. I do think that there's a conversation around the heart of the relationship is human to human. So everything that's related to data entry scheduling, I think that happens. I think you're absolutely right.
My concern is around cyber fraud and what happens there. And my concern is around anything that diminishes the advisor to client relationship. And so you lose some of that fabric if you don't have that conversation, that's like, hey, Callie, I need to schedule a time for my mom and I to come in and talk about her finances. Like, how's it going, how the kids stuff like that. And I think that I'll give you an example that's maybe contrary.
I just switched banks. I went from a very large institution that everybody talks about, who's always in the press to a local bank and the local bank. I need to get cash. I need to get small bills. I went to Mexico over the holidays.
I went and I walked in. There was no teller line. There was no. Like, I walked in and there was a person at the desk who just smiled at me and was like, how can I help you? And it was like it was a shockingly disruptive experience, and it was 100% human.
And I think that for the best Rias, the best broker dealers, the best people serving clients, they are going to find ways to capture that ethos. And it's with their people and it's with like this human to human interaction. So yeah, I'm a little skeptical.
Richard Walker: 20:24
Yeah, I.
Patrick Hannon: 20:25
It's so it's so focused on our space.
Richard Walker: 20:26
Yeah. Look I agree. I mean, you don't want to replace the human aspect of the relationship, but I'm also thinking just minor simple transactions of do you have this availability or do you have this capability etcetera. And it's kind of like fax machines, right? They have to be connected and they have to know to call each other.
I love far-fetched ideas. I love brainstorming on these things. Doesn't mean they're going to come to fruition.
Patrick Hannon: 20:48
Maybe, maybe, like, I'll noodle on that and we'll think about where that plays. Data entry has got to get cleaned up. Like I think that's where that is. I'm just so skeptical. I'm so not it's not skeptical about the idea.
It's I'm so nervous about cyber fraud, scamming, phishing.
Richard Walker: 21:05
Yeah.
Patrick Hannon: 21:07
In our space that again it's why it's why we still call the confirm wire numbers before we transfer. It's because you want to know. You have to call me. I can't I can't just wire this for Rich. You got to call me back at my number to confirm it's really me.
And. Yeah, like it's why there's still humans in the loop. Humans in the loop? There's your AI catchphrase for the day.
Richard Walker: 21:30
You know what? That's actually how form extract works. It has human in the loop. There you go. One of the things that makes it so accurate, and I love one of the things I love about tech is to think old school like you're saying, because a lot of people don't think about that.
In fact, I've seen a lot of technology companies come out and say, we're going to change the world as we're super disruptive, we're going to change everything. People don't want that much change. They want incremental improvements. And so the old world way of thinking, with some incremental improvements is usually more successful than I'm going to change everything for people.
Patrick Hannon: 22:01
Yeah, 100%.
Richard Walker: 22:02
So tell me about Catchlight. You said that's another company in your portfolio. What does that one do?
Patrick Hannon: 22:07
Yeah. Catchlight solves three problems for financial advisors. The first is to understand lead propensity scoring. So like you, give me your name and email address rich. And we're going to go out to third parties because this is nothing to do with Fidelity data.
I don't know if you're a Fidelity customer or not, but we just take your name and email address, and we're going to score you on a scale of 1 to 100 based on data that's publicly available. We're going to say, how likely are you to use financial advisor to pay for financial advice? And what we're just doing is we're just we've built models that look at you and say, do you look like someone else who pays for financial advice? And so we saw you on a scale of 1 to 10 for the financial advisor, it's really helpful for them to understand when they're buying leads or they're looking at a list of leads or they've got seminar leads. Who should I focus on?
Because they all look the same on on paper. Or maybe you're looking at a, you know, a conversion from a planned participant at A4K that you're advising on and you want to bring that person in. Like, who should I talk to? So that's problem number one is lead propensity scoring. Catchlight's second problem that they solve is a tear sheet.
So like tell me about Rich. Does he like pickleball. Does he like tequila. Does he like, you know, long walks on the beach or. I'm kidding there.
But it will give you financial topics. So maybe you're philanthropic. Maybe your needs are estate planning. Maybe you've got kids in college, and we're making predictions on all of that so that someone can walk in and say, hey, here's some topics that I are likely that I should talk to Rich about. And the third is then using AI, saying, hey, if I know if Rich is a good lead and I know what I should talk about, now, I can generate a custom email specific to Rich that's compliant that we should we should offer up.
And so you got to understand those two first parts before you start applying generative AI to do cold outreach.
Richard Walker: 23:49
Man that is awesome. Okay. So two things I want to ask about this. The lead propensity I think is what you said the likelihood of them to buy financial planning services. To me that's backward-looking.
It's looking at what you know about them. Right. But do you do you understand as the I understand what brings somebody from a five to an eight or a seven to a ten and whether they're there, whether they're at that precipice to jump over.
Patrick Hannon: 24:14
Yeah, we're what's interesting is that we'll monitor folks on a monthly basis as we're kind of refreshing the data in the background. And we can see changes in that. We can see changes in somebody doesn't like this is again, it's incremental. It's not like someone just flips over overnight. But you see you see their zip code change.
And that can tell you about their financial status and who they look like and where they're going and what the trajectory is. But if you if you back up for a second, if you look at the US population, very small portion of that is well served by our financial advisor community today, given the constraints on the financial advisors. And you have to look at your customer base as a financial advisor and say, is this profitable or not? And that depends on how you've built your practice. But understanding in a I'll give you a couple examples, but understanding if you're buying lists.
So we see on the on our buys all the time like people spend like $10 million a year on leads. We can look at those leads and we can tell you like 90% of them are, are terrible, you know, and it's like, where should you focus your time on the millions of dollars you're spending? Or maybe you're not spending millions, but hundreds of thousands on, like, buying leads. Like maybe you should get a new lead provider, or maybe you should focus on these specific leads more. You look at the distribution curve of the US population.
We want to help you build and deepen your moats with your existing customer base by adding more people in that. And so the application of this can be anything from who you see next to each other at a seminar based on the data. It could be the next phone call you make. Or it could be it could be a large institution, like a bank or a large 400 K provider saying, hey, we want to move people from one line of business to a second line of business. You know, we've got 100,000 people in our in our ecosystem who are the first ten I should call and helping people be more efficient in their day.
Richard Walker: 26:08
You know, I'm going to give you guys a little bit of kudos because I know one of your customers and they're they've been using your catchlight system for a year or more or something like that. I'm going to leave them nameless. I don't want to disclose information.
Patrick Hannon: 26:20
I don't know anything about this where you're going with this. So I don't know.
Richard Walker: 26:22
Know I know you don't. I mean, I talked to somebody at their firm last month and I was talking to him about this and he's like, oh yeah, we've been using it for a year. They are wildly successful. Like they hit their AUM goal for the year in like September or October. And now they they're looking at doubling it because they are really, really good at lead generation and they're just growing like crazy.
And I'm sure that Catchlight is helping them really understand who to work with and who to focus on. I'm curious about something else, and this is maybe a really stupid application. I love these types of concepts too. Do any of your customers run Catchlight against their current customer list to evaluate them? Absolutely, absolutely.
Patrick Hannon: 27:05
It's fascinating when you do that. Some people do that for share of wallet like okay, what does Catchlight predicting the share of wallet to be or the total investable assets. What do I have. You know I there's a client that's a bank that when we were in the sales engagement with them, I just assumed that they would be interested in interested in conversion from Their commercial banking side of their business to the wealth side of their business, like in banking and in lots of places. Those conversion rates are astronomically small, like low single-digit conversion rates.
Between that, the lines of business. I assumed that that would be the primary focus of deploying Catchlight. They looked at and said, no, we've got 150 commercial banking clients that we don't know enough about. We just want to market to our existing client base itself. And we're sitting there going, like, we can help you double your conversion rates because like getting doubling a low single digits is not hard for the Catchlight team.
So it's like, yeah, we can if we can make that an incremental growth like the ROI here is through the roof. And they're just saying no. Like we need to protect what we've already built and we want to serve those clients. Seminars is a great example for your traditional RIA that maybe have a couple hundred households. What should I do?
Should it be a I keep picking on myself for pickleball? Should it be a pickleball tournament? Or should it be a wine? A wine event with a sommelier? Should it be a golf outing?
Like, what are my clients actually interested in? Because and this is where like ChatGPT is great. Like if you're a financial advisor and you haven't asked ChatGPT to come up with ten different client-facing events, that is a great use of Claude or ChatGPT or something like that. But client events in our industry, they're kind of all the same. They're kind of all suck.
And so like finding ways to engage with clients is so incredibly important today, because that's the relationship that we keep coming back to between the advisor and the client. And you do that through like fun things. And it's not always a Big Ten football game for all your clients. Like we know that to be true. So like having a lens into not just the financial considerations but like some of the social engagements is just that fabric relationship building that I think Catchlight really helps with folks.
Richard Walker: 29:15
Yeah. No, it sounds fascinating. And I was actually thinking of, you know, when you rate your clients as a, B or C clients and you may want to get rid of your C clients or elevate them to be your A clients and would catchlight help you do that? And it sounds like it does, because it helps you understand your client better. And like you said, share of wallet.
That's amazing.
Patrick Hannon: 29:32
It's interesting when. So we'll do a blind. We've done a blind test for some really large firms where we say, hey, give us two lists, give us a, give us an A, a list and a B list and a can be clients or prospects and B is the opposite. We say don't tell us which is which. And when we get the data, we process the data on all the people that they send.
You see a distribution curve. And for the lead list you see a very natural distribution curve, right. It looks like your standard distribution curve. But the client list, it's pushed to the right. And so you can see they don't win in the low catchlight numbers.
And it's a curve up. And you can see where the bulk of their wins are. But it's really fun because we don't need to we can we can present data back to them and say, you didn't tell us which is which, but these are your this is where you're winning. And you can look at where like what specific catchlight score. And some advisors just win between a 70 and an 80 and they don't really win 90.
And like we have to dig into why aren't you winning the high numbers. But like hey, if you get a 60 to 80, just like go call that person ten times and ignore the person who's a ten because like, you don't have any of those folks in your book anyways. So that's where the comparison between the two can be pretty neat.
Richard Walker: 30:40
All right. I'm gonna ask you a very different question. We're going to run out of time, but I have to ask this kind of question of you. I presume that a lot of the portfolio businesses you're working at have some AI component, and I'm sure there's others that don't. But I'm I'm looking at the world through the AI lens now.
And what I've actually said is my company is AI-driven. Yes. We have our first product out. That's AI driven. We have another one in the on the in the works.
I'm not going to go to market and say, hey, we're AI. That's not the goal. The goal is to be AI-driven, because AI is a tool set to empower us to do better, work faster, work higher quality work. And it's the same for our customers. Help them accomplish the same.
I'm just kind of curious from your standpoint, do you believe in that kind of model? Do you think others should follow that model? What do you think I should mean to any given type of company out there?
Patrick Hannon: 31:29
I think all companies will use AI in some component, and I think. A few companies will have a solution that requires AI to solve the problem for the customer. And let me give you an example. There are different types of AI right. And so we talked about ChatGPT generative content.
That is a single type of app. There's other AI that is doing anomaly detection and anomaly detection. If you look through, you know, imagine you're a large bank and you want to look through millions or billions of transactions and say, show me which ones are related to fraud, scammers, etc. that is an entirely different application of AI. And that's an example that's like unique, like you need an AI solution that can do that because it can't really be handled in other ways. And so all businesses will have AI components to them.
Some will be powered by AI, some will be the solution will be very distinctly unique, uniquely AI, and others will just say, like, you go back to my former business risk lives for nitrogen. Like that's a portfolio analytics business. And they have wonderful ways that they can use AI to apply to their business to strengthen the client advisor relationship, to do lots of things, to build integrations. But the core solution there isn't really AI. It's a beautiful way of translating risk to the customer, which you may or may not believe in the model and like whatever.
But like it's not a uniquely AI. Like there's a lot of ways to apply AI to make that business better for its customers and its advisors. But there's a difference there between something like safer, which has built its own small language model with data. You know, of that ad review like that's distinctly AI in a way that other businesses are going to leverage AI to further the business. But it's not an AI business.
Richard Walker: 33:16
Yeah, yeah, I agree, I don't think anybody should move away from their core business model. They should look at AI as purely a tool. It's like saying, oh, I'm a hammer company now. Well, if that's the right tool to help you move forward, fine. But it doesn't mean you change your whole company around it.
And when I say I driven, I just mean the mindset set.
Patrick Hannon: 33:35
Yeah, yeah.
Richard Walker: 33:36
My team, our tools, whatever we can build.
Patrick Hannon: 33:39
Everybody should be AI-driven. The caveats I always give to financial advisors are you got to be really careful as soon as it's client facing or includes client data.
Richard Walker: 33:47
Yeah.
Patrick Hannon: 33:48
The biggest the biggest threats to firms today I think are largely compliance and cyber.
Richard Walker: 33:54
Yeah for sure. Oh, man I want to keep talking but we're going to run out of time. So before we wrap this up and I have another question for you. What is the best way for people to find and connect with you? Patrick.
Patrick Hannon: 34:04
Yeah. Find me on LinkedIn. You can connect with me on LinkedIn and drop me a note. So, Patrick Hannon, Fidelity Labs, you can certainly shoot me an email. That's PatrickHannon@FMR.
F as in Frank M as in Mary. R as in Robert. FMR.Com is probably the easiest way to get a hold of me. I'm not on Twitter anymore.
I'm not or X it's really just probably LinkedIn.
Richard Walker: 34:26
So what is the FMR? Fidelity Management Research. What is it? Is that it?
Patrick Hannon: 34:32
Yeah.
Richard Walker: 34:32
Okay. All right. Here comes my last question. Who has had the biggest impact on your leadership style and how you approach your role today?
Patrick Hannon: 34:40
Yeah, I have a number of mentors. And like, the people who instantly come to mind are folks like Lori Hardwick, Aaron Klein, Drew de Marino, Lauren Brockhaus, Brad Jodry. And you had like, tipped me off a little bit before on this question, but like, this is what I would say about that. And they've all had impacts in a variety of ways, but they've always all those folks looked at someone who was, you know, junior to them in some ways and sent the elevator back down. They gave me opportunities, they gave me chances to fail, and safe environments.
And so when I think about this question in particular, I just ask everybody in the audience, think to themselves for the next 10 seconds about one person that's more junior who just needs a shot, and that could be a shot to fail, right? But they just need an opportunity to enable growth for them and to think about how to do that, how to how to send the elevator back down. And that's what I'm thinking about with the mentors or the interns now that I get to interact with at Fidelity, which is how do I give people opportunities to fail in an awesome environment? Or if they don't fail, they are awesome. They just crushed it.
And you look like a hero for putting them in a position to do that. So I think about that question and I would thank all those people again. But really, to the folks listening, think about ways to send the elevator back down.
Richard Walker: 36:00
Do you think that depends on how you set up accountability for that person so that you can measure the failure or the success?
Patrick Hannon: 36:08
People have it or they don't. And like the way to find that out is to to give them opportunities and give them opportunities to succeed. Maybe that's the more positive way of spinning that, but you give them opportunities. And I think it's important for you to tell people like, I'm giving you a hard goal, a stretch goal or a stretch objective here, but I don't want to coach you, rich on how to execute this, because the more I do this, the more I realize that if I just tell you that this is the thing I want you to accomplish, and I don't focus on, like the steps in that. Man, people come up with creative solutions that I would have never thought of, and you just have to be available to them to help.
Richard Walker: 36:46
Well, that's really the magic of delegation. Let others figure it out.
Patrick Hannon: 36:50
Yeah, but it's also that safe environment where they can fail. And again, you learn quickly who has it and who doesn't.
Richard Walker: 36:56
Yeah, yeah. Oh man. There's another topic I'd love to keep going on.
Patrick Hannon: 37:00
You could hire only doers, right? Like every there for every ten people who have an idea, there's one person that will go out and execute. And then in, in. I was well served with all of those mentors. Like my favorite thing saying to Aaron Klein was, I'll take that off your plate.
And let me tell you, as a CEO of a busy growing company, I could tell that the man loved it when I said, let me take that off the plate. But then I had to go execute against it. And, you know, sometimes I most of the time, hopefully, I did well. And a few times I failed, and it was okay.
Richard Walker: 37:31
Yeah. No that's awesome. Thank you for sharing that. So I want to give a big thank you to Patrick Hannon, VP of SaaS commercialization at Fidelity Labs, for being on this episode of The Customer Wins. Go check out Patrick's website at labs.fidelity.com.
And don't forget to. Don't forget to check out Quick at Quickforms.com, where we make processing forms easier. I hope you enjoyed this discussion. We'll click the like button, share this with someone, and subscribe to our channels for future episodes of The Customer Wins. Patrick, thank you so much for joining me today.
Patrick Hannon: 38:01
Thank you for having me. Why don't you send the elevator back down, folks?
Outro: 38:05
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