Ep. 146: Leveraging Artificial Intelligence to Improve Prospecting for Advisors

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Come on in, sit back and relax, you’re listening to episode 145 of the WealthTech Today podcast. I’m your host, Craig Iskowitz, founder of Ezra Group consulting, and this podcast features interviews, news, and analysis on the trends and best practices all around wealth management technology. Our theme for this month is artificial intelligence, machine learning and predictive analytics. We’ve lined up an impressive list of guests for you, they’re going to talk about the latest advances in AI & ML in the wealth management space. Research shows that 70% of the devices we currently use have AI already built into them, and 52% of organizations have accelerated their plans to incorporate AI in some capacity, so this topic is very timely.
Our guests today are Wilbur Swan and Yelena Melamed from Catchlight.ai, but first let me expound on how important data is to the success of any AI-based initiative or any technology written program at a wealth management firm. That’s why Ezra Group launched our Data Assessment Service to conduct in an depth review of data sources, downstream consumers, data utilization analysis for enterprise wealth management firms and deliver a comprehensive strategy and roadmap to get your data architecture under control. For more information on Ezra Group’s Data Assessment Service go to ezragroupllc.com

Companies Mentioned

Topics Mentioned

  • What is Lead Optimization?
  • CatchLight Score
  • Financial Complexity = Higher Conversion Rates
  • Dynamic User Interface
  • Advisor Use Cases
  • Integrations

Episode Transcript

Craig: All right. I’m excited to introduce my next guests on this episode of the podcast. We have Wilbur Swan CEO of Catchlight, hey Wilber.

Wilbur: Good afternoon.

Craig: And we have Yelena Melamed, head of product for Catchlight. Glad you guys can make it, glad we’re here. Where are we all calling in from?

Wilbur: Boston, Massachusetts.

Craig: Love Boston. Great place to be. How’s the weather up there now?

Yelena: Your typical spring day, sunny and forty.

Craig: Sunny in the forties, right? Barely above freezing point

Craig: That’s what we love about Boston. Cool. Alright, so I’m glad you guys are here. Let’s just jump right in. Can you please give us the 30-second elevator pitch for Catchlight?

Wilbur: Sure. Happy to do so. Catchlight is the first of its kind lead optimization solution for financial advisors designed to help them accelerate their growth. Very simply we use AI and data to analyze advisors leads to recommend to them who to call, how to pitch and what to pitch week after week. So each week they’re getting better and better at finding their best prospects and converting them.

What is Lead Optimization?

Craig: I really thought this was interesting when we met a couple weeks ago and I saw your product and I was really excited by it. So can you explain a little bit more about the lead optimization? So you’re not lead generation, which is very different. There’s a lot of products that do lead optimization. Can you explain the difference?

Yelena: Absolutely. So we are analyzing at this point a hundred thousand successful conversions or paid advice relationships through a random forest model which is our machine learn model and aligning and assessing what characteristics really make up a reason for a person to convert. We’re then taking that insight and identifying and scoring similarities across advisors lead lists. And that allows us to hand a score over to an advisor for them to organize their thinking about their prospect list and really focus their attention on their best bets.

Yelena: For us what’s been interesting is watching the advisors use this day to day and the more they use it, the smarter it gets it really tailors itself to how an advisor does business. It’s important for us to help advisors get started pretty turnkey. So when they get started, they’re using the baseline model, but the more they use the model, the more they use the product, the more it learns in how they’re prospecting and where their niche is, and each advisor is unique in how they do business. And so how we are prioritizing is different. Also differences come from what we learn about prospects they work with. And we update the way our model interacts with them, the way our model interacts in general on a pretty regular basis.

Wilbur: You make an interesting point though, Craig, we’re not doing lead generation. We’re not supplying them with net new leads. What we’re doing is analyzing the leads that a given advisor has in their pipeline. I would say what we found is over the COVID period, a lot of advisors have gotten into, you know, how do they better market themselves? How do they create a brand? How do they personify that brand on a website? How do they start doing social media? And as a result of that, not surprisingly their supply of leads that they have either in their CRM system or in spreadsheets has grown larger and larger.

Wilbur: Not surprisingly given, we’ve all been talking to each other, like we’re talking to you right now. A lot of that’s been over Zoom and other digital connections versus face to face. So you don’t know as much about that person. It’s oftentimes they just know really a first name, a last name, an email address, a phone number and maybe one or two other pieces of information. What we’re helping them do is understand what does that entire prospect look like? And how does that prospect sit as going to put it nicely. Are they really a top five prospect or somebody that’s, you know more in the middle of the pack and might wait for a call in a later week.

CatchLight Score

Craig: And that takes us right to the next topic, which is the Catchlight score. So this is the score that advisors would use to rank their prospects as to which ones to call in what order. So how do you calculate the score what’s going on behind the scenes,

Yelena: Craig, we are looking at characteristics which do change over time that identify similarity in past conversion events to characteristics of people. I think the way I simply think about it is the more complex someone’s financial situation, the more likely they’re gonna have a higher score but it is not a rules based model. It is a learned model. And so we are really leveraging the machine learning in order to calculate what that score is. So the higher the score the higher the propensity of someone to need a financial advisor to support them, because it’s likely that they are in a very complex situation, more bedrooms, more children, maybe pets. Today as of the moment we’re speaking with you there are 42 different data elements that go into that score. But we are getting ready to ship an update to our model, which we do on a pretty regular basis, that version is going to have 50, essentially what drives people to convert has also evolved over the duration of the pandemic that we just talked about as well. So we are constantly looking at what are the trends in conversion behavior and reflecting that back to advisors.

Craig: Now, let me just pick out one thing you said, Yelena, I think is really important. It’s not rules-based, it’s a learning model. I can’t tell you how many companies I hear., they say we use AI, we’re AI-based. Once you dig onto the covers a little bit, it’s really just a rules engine. It’s just a big if then else machine. So let’s talk a little about your learning model just for a bit, it learns as it goes, the more you use it, the more an advisor uses the product, the more accurate and the better it is. Would that be a true statement?

Yelena: That’s right. So the more advisors use it, the better it is across the board, but it’s also the better it is at reflecting the conditions for a particular advisor, as we’ve said before. And you know this better than I, advisors are all very unique in their prospecting approach and their marketing approach. And so what we’re hoping that it provides back in value is helping them work as they already work, but be more efficient with their time.

Financial Complexity = Higher Conversion Rates

Craig: And that’s huge. And that goes back to something. We talk about a lot in my company, which is scalability of advisors. Scalability isn’t just about how many clients you can support, it’s how much time you can get back from your technology to spend with your clients. So if I’ve got a tech that reduces the amount of time it takes me to evaluate a prospect or review my daily is to prospects by half that’s an extra, 30 minutes or an hour, I get every day that I can do other things that makes advisors more scalable in my mind now can we talk a little bit about the, the higher score was a higher propensity to need an advisor. Now you had told me earlier that higher financial complexity correlates to higher conversion. Can you talk about that?

Yelena: Sure. So what we’re seeing, and again, the changes over time that there’s a number of characteristics and surprisingly, it’s not the characteristics that we commonly think about, higher income and therefore it is characteristics that are part of everyday complexity life events, whatever they may be of the complexity around having to support multiple dependents and whatever shape that may take. Again do you have equity? Do you have multiple properties? Do you have properties across state lines and I’m giving you just some examples. The reality is that how important each one of those data elements are as an attribution to that score varies and varies over time. It also varies across advisors, I’d say.

Craig: If you’ve met one advisor, as they say, you’ve met one advisor. So everything’s gonna be different it’s gotta be tuned. So we talk about, we talked about the Catchlight score, let’s talk about what is the Catchlight system? What does it mean? Is it is it a platform? Is it an engine? What are you guys looking to build here?

Yelena: We really do see it as an insights engine. It is data meant to help advisors gain more efficiency. Now we do have a platform and we like to think about it as an easy to use fun workflow but it is just one avenue and how advisors may choose to use our capabilities. We really are not focused on creating a platform here. We are here to support advisors in wherever they work already with the types of data elements and AI and analytics that makes them more efficient.

Wilbur: I could put another spin on that. We do have advisors right now that are interacting on a day to day basis with our Catchlight UI, right? Which nicely conveys the analytics that we’ve just talked about, very simple to use. It looks like a Yelp interface. It’s basically three screens of workflow. And the idea there was, we want to keep this super simple for advisors, something they could really pull up if they said I have 20 minutes and I want to figure out, who’s the best person I can call, what can I pitch them on? And how do I pitch them? That’s basically what we’re trying to convey in that user interface below that empowered by all the data insight engine that Yelena is talking about are all the analytics that are going on, which some of the larger firms are starting to tap into on a larger scale.

Dynamic User Interface

Craig: You mentioned the Catchlight UI. One of the things I liked about it was you selected the fields. So looking at the fields of a prospect on the screen, they’re not static, you designed them based on which ones advisors use the most.

Yelena: We spend a ton of time with advisors in different shapes of data to really monitor what helps them make more efficient as opposed to taxes their time even more. And so that’s where we definitely differentiate because we recognize that the more actionable we can make the data, the simpler it is to read, the simpler it is to interact with the more useful. And so to your point we really boil down the essence of what was important around the data into that profile. But we’re also aware that advisors get to know their prospect as they work with them more, and they see the data, they converse with clients. To them it’s a living breathing thing, and we wanna make sure that they can take advantage of AI and AI can improve based on conversations they’re having. So we’ve built the screens to be living to your point and being able to take on the learnings they have on the data to be able to push feedback on the data, back to us and ultimately use the product more in order for it to be smarter for them.

Craig: This is something I talk to a lot of companies about, especially ones that as they grow, they’ve got so much data that they don’t really use. For example, companies that have a lot of reports available. Sometimes they have hundreds of reports available and say, well, how do advisors know which ones to use they say, well, they kind of go through it. They look around, they see they talk to their friends. Why don’t you show them based on all your clients, which reports are most used in rank them that way. So, you know, I like that you’re doing that. You’re building your system to show here’s what other advisors are doing. Here’s this field that they like, maybe you’ll like them too, and the odds are high that they will. Can you share any statistics with us about improvement conversions or hit rates or anything around which again, realizing you’re just a startup, you don’t have that many clients yet, but what kind of things are you seeing across your base?

Yelena: So the the firms that we work with today maybe I’ll take a quick step back and make a point that data is very important to us, the quality of data is very important to us. And we tend to back test the data back test the conversion success a lot with the firms that we work with. And so what we’re seeing on a pretty regular basis that typically we’re able to pinpoint a mix of leads and clients if you will and bubble up their clients already won to the top of the list with our scoring methodology which is just another way for us to confirm that this is a methodology that works and it is effective. Furthermore advisors tend to use the product in very diverse ways because to your point, they are very diverse.

Yelena: Some are seeing two to three times better conversion results. So we see advisors going from like a 3% conversion to a 9% conversion success, which is exciting. We worked with an advisor who is very focused on his digital marketing efforts. And so far before using the data that Catchlight makes available he was getting pretty mediocre engagement rates from the content he was shipping out and he spent a ton of time building that content. So it was very important to him that he delivered that content to the right clients and the right prospects. And so leveraging the data that he lifted out of Catchlight. He was able to get to five times the engagement rate that he had before, which is really exciting to see, because it really just makes more efficient, not the prospecting efforts that he has, but also the work that he’s already attributed to building the content, building the engagement and really putting to work the leads that he already has in his phone.

Craig: That sounds impressive.

Wilbur: Just want to mention that we are going to be doing a bunch of press next week related to T3 in Dallas, which we’re excited about as part of basically a public relations related launch. But we have been out actually working with advisors in production environments since last summer. And we’ve had paying clients since October. So we’ve actually got real experience based on real data based on people who are putting real money into the system.

Craig: It’s always a different game when you’ve got paying clients. They have different expectations.

Wilbur: Yeah. Yeah.

Yelena: I’d like to believe that we continue to push ourselves and have stronger and stronger expectations of ourselves. So for us, what’s important is what other data can we get in front of advisors? What else can we make more efficient what other capabilities tools, features don’t tax time, but again, create more capacity for an advisor to react to that point in time conversation, but also reflect back on their entirety of their prospecting funnel. And are they going in the direction that let’s say even the practice management exercise would suggest that they do on their existing book, right, are they going into the direction to build the book of the future that they want?

Advisor Use Cases

Craig: So let’s talk about use cases for people listening to this, RIAs and other wealth management firms who want to use your product. Can you give us a use case for how it would function in a real life scenario?

Wilbur: The simplest, and I’d say the majority of our paying users are single advisors who’ve bought a single seat for themselves, and they’re saying on Monday morning, okay, I’ve got 20 minutes. I want to think about how I might grow my business, very simply who is the best prospect I should call. And as I said before, what would be some good concepts to pitch that person? And how should I engage with that person? Basically a jumpstart on how do you optimize your sales results in a given week.

Wilbur: Once you introduce a firm to these types of capabilities and this type of AI marketing teams oftentimes start to think about the application of this. So as Yelena said they’ll think about, okay, so let me look at a larger pool of leads and think about how do I begin evolving my marketing to more personalized and segmented marketing. So we’re providing insights to them to say, okay, well, if you’ve got a particular piece of content that’s related to charitable giving, for example, here’s how you might filter this list of leads down to a group that it’ll particularly resonate, as Yelena said, what they see, not surprisingly in a lot of industry studies validate this, is that when you reach out to people in a personalized way, your response rates go up proportionally.

Wilbur: Last we’ve seen, and this is really something we’ve been working on in the last few months is that as you get several advisors at a firm using the solution oftentimes the seat level folks, the executives begin to look at this and say, this is really pretty interesting, to date a lot of this pipeline data that I would like to see has oftentimes been in a given CRM system dedicated to a given advisor. And what they say is this has given me a unique ability to begin and understand in aggregate across my firm or across several advisors in this case, but potentially across the firm. What does the pipeline look like? Does it compare to where we think the strategy of the firm is going, is the pipeline big enough to support global growth we aspire to, how could we help our advisors begin to shape that pipeline? So it’s even more productive for them, and in turn for the firm,

Craig: I’m sure a lot of companies would love to know that cause they’ve got these pipelines, they’ve got these maybe large CRM tools that can show them how many prospects there are, but they can’t rank them or rate them, or really understand besides unless they have some really wild guesses, what the actual value of them, of these prospects are and which ones they should be calling first.

Wilbur: A lot has happened in this space in the last 10 years CRMs getting very, very sophisticated with data and AI, large companies. So we’re obviously incubated in Fidelity Labs, large companies do these types of analytics and optimization exercises for their own purposes. I personally think it’s great to introduce these type of capabilities to the to the independent advisors of the world

Craig: Cause they need help. Surely do. So you’ve got three use cases, the advisor use case, and I wanna save some time which prospects should I call. You have the marketer’s use case to generate personalized marketing and we know personalization increases response rates, and we have the C level, if you’re at a larger firm that has an executive board that say, well, Hey, we’ve got these goals to grow at a certain percentage. And it helps them understand if the pipeline across all their advisors, which could be one advisor or hundred advisors or thousand advisors. If that pipeline is large enough to deliver on our goals. Would that be a fair summary?

Wilbur: Yep. Perfect.

Integrations

Craig: So now we’ve got all these tools, we’ve got the inside engine, we’ve got the Catchlight score. So there’s a lot going on here and advisors really want things to be easy and simple. They want it to fit into their existing workflow. They don’t want to have to go to a separate system or a separate even a separate browser tab. So how does Catchlight integrate with existing platforms that advisors are using that?

Yelena: Great question. Totally agree. We are trying to reduce fragmentation, reduce lift for advisors. And so as a result we really focused a lot on our integration roadmap. We recently launched an integration with Redtail. We are HubSpot enabled and if you weigh the shape of the roadmap with regard to integrations, it’s a pretty heavy stack of things that we want to get done, but ultimately we’re underpinned by API ready solution. So we are ready to integrate and are working with a number of our prospect and customers to build into their existing solutions. Some of them to your point is their proprietary data sets. And some of them are existing capabilities such as CRM, digital marketing tools they already use.

Craig: There’s so many tools going on so much to integrate with, but —

Wilbur: We were talking to a client last week who is building a their own platform, for all of their advisors, which we see the larger firms doing, super interesting. I think it would be really interesting to see what happens with solutions like Snowflake as places where inside engines as ourselves to operate really effectively, we use Snowflake here extensively within Labs and see potentially as a place where clients could access this data, manipulate it how they wish and then serve it up wherever they wish.

Craig: So bringing up Snowflake, we do a lot of work with data, data assessments for, for large enterprise wealth management firms. And of course, Snowflake comes up a lot, whether they know how to use it or not is a different story. Can you explain how Snowflake is built into your infrastructure?

Yelena: It’s twofold. So we use a separate and proprietary snowflake instance to do our R & D that’s first and foremost. So a lot of our AI tends to start in conversation with advisors, and then we pull a lot of data and do a lot of work in an R & D environment. And then when we think about how we pipeline the data in and work with the data that we have, that’s kind of the secondary space where we leverage Snowflake as well. And so when we think about the various ways we can integrate, Snowflake is definitely a shape of of something that we could do.

Yelena: Another is an API. And so for advisors that are probably on a larger side or in the midsize APIs are more comfortable to your point. When the larger enterprises come in and talk about their existing data and want us to use their data as part of they experience and want to take advantage of the data that we offer, Snowflake is the more comfortable base for them.

Craig: Thank you. I just like to dig in a little bit on the tech side, not too much, but a little bit to understand how different firms are working with data. And we do a data assessment service for enterprise wealth management firms, and we just had a a webinar, which you can find on our website, it’s called Data Pollution, How to Keep Your Data Lakes from Turning Into Data Swamps, a lot of Snowflake discussion on that webinar. So you guys are AI based. You’re, you’re running with a a random forest model underneath for statistical analysis. And there’s a lot of work on AI based assistance and other industries and other markets. How, how is Catchlight doing that? Would you consider yourself an AI based assistant? And are you moving in that direction?

Wilbur: I think, you could look at what we’re doing right now as a smart sales assistant for the advisor where we’re basically taking minimal data about a lead, turning it into a rich profile about the lead, and then doing the AI that we talked about to prioritize that lead in terms of which one to call next. I think there’s a, a lot that could be done for an advisor potentially like an AI based assistant to help them better prepare for meetings to help them better service clients. I think the trick, like any of these things is it takes a lot of data to drive those assistants which I think we you know, being within Fidelity Labs, a part of the ecosystem here can get access to I would say what’s interesting in other industries, you look at medical research or you look at what’s going on in Amazon with Alexa or Apple with Siri, I think a lot of people are getting used to interacting with the AI based assistants increasingly over time. So I think if you look at the next 5-10 years that could be one of the big productivity drivers in the financial services space.

Yelena: What we’ve done, Craig and you saw in our product is try to figure out how we can marry the data and research AI that we are bubbling up with thought leadership on engagement. And so really are thinking about what makes a particular person stand apart. Maybe they’ve done charitable giving in the past and how can an advisor use that information from a pitch perspective, right? Maybe that means that they tie that out to a conversation about charitable giving and maybe a specific type of charitable giving, perhaps it is a business owner who’s done charitable giving. And so the combination of those two assets in terms of thought leadership is a very different type of a discussion, very different combination of things that you may present as a value add.

Yelena: Even in that first conversation as an advisor that is you can help someone save money and be very effective right off the bat. We are also bringing in thought leadership on the type and style of engagement. So from research that we’re aware of is certain types of people and sometimes it’s by age, sometimes it’s by other types of traits respond better to email versus phone. And so some other aspects of what we do is to try to tie some of the metrics and characteristics that we know about a person into, how can an advisor think and shape all of that into the best way to interact with someone. So they really stand apart versus somebody else. And that’s really where we think that we can offer more value.

Craig: That is excellent. We are almost of time. So you’re launching next week. Congratulations. Can you talk a little bit about that and your pricing model?

Yelena: We’re excited. So as Wilbur had noted we’ve been working with advisors for a while now, and we started sales in October of last year. So it’s really exciting to really dip our toe into the PR launch and really talk about ourselves more widely with more advisors, more shapes of firms. And so that’s great. We’re excited to be at T3 hoping that folks can find us in the exhibit hall stop by we’re at booth 811. And I think it’s exciting to be able to talk about a product it’s equally exciting to talk about the wins that advisors that have used the product to date have had. Pricing, we offer both monthly and annual, annual at a discount is at $1,500 per seat.

Yelena: What’s exciting there is, as we talk to advisors and they really evaluate the price, they value the value that we deliver in form of the product. They talk about really takes one client win to make it all worthwhile. And that that’s exciting to hear. We continue to listen to the feedback and what’s been great about kind of our first crew of advisors and those that will join is we continue to look to them as those folks that shape the product in a pretty continuous way very directly in the product itself, but also through conversations with us. And so I’m excited for next week, I’m excited for the launch and I’m excited to talk to more advisors about it.

Craig: And I will see you at T3 as well next week. I’ll be either the whole week. So look me up, I’ll be walking around. I’ll come find you guys. So how can advisors or other enterprise wealth management firms get in touch with you?

Wilbur: The easiest way would be just to go to Catchlight.ai, our website we relatively easy to contact us from there. But we could also provide you with contact information if you want to add it to this book cast.

Craig: Sure. We’ll put that into the show notes. So it’s catch Catchlight.ai. Yelena and Wilbur, thanks for being on the program.

Click here and schedule a Discovery Session to find out how Ezra Group can help your fintech firm grow revenue in the wealth management space.

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The Wealth Tech Today blog is published by Craig Iskowitz, founder and CEO of Ezra Group, a boutique consulting firm that caters to banks, broker-dealers, RIA’s, asset managers and the leading vendors in the surrounding #fintech space. He can be reached at craig@ezragroupllc.com

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