Uncovering Client Personality: More Than A “Type”

Episode 12 May 24, 2023 00:25:09
Uncovering Client Personality: More Than A “Type”
Purposeful Planning Podcast
Uncovering Client Personality: More Than A “Type”

May 24 2023 | 00:25:09


Show Notes

Delve into the fascinating realm of understanding client personalities and their influence on financial decisions. In this session, we explore how personality traits can accurately predict saving, spending, and investing behaviors, enabling you to anticipate your clients' future actions and enhance your advisory skills. Learn about a variety of methods to measure personality, including observations, structured interviews, and tests that can provide valuable insights and uncover the unique characteristics of your clients, enriching your dialogue with them about money-related experiences and beliefs. 

About Our Speaker: Sarah Stanley Fallaw, Ph.D. is the co-author of The Next Millionaire Next Door and the founder of DataPoints. She works with firms around the world to help them understand their clients’ personalities. Sarah received her Ph.D. in Applied Psychology from the University of Georgia in 2003.

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Episode Transcript

JOHN A: Welcome everyone. This is John A. Warnick, founder of the Purposeful Planning Institute, and I'm delighted today to introduce you to Sarah Fallaw for this conversation around Uncovering Client Personalities. I think this is going to be a very valuable session and it ties in to the podcast that we’ll release simultaneously, the program that we've recorded with Maggie where she shows kind of the application of a portion, really just a small portion, of the tools that Sarah's making available to advisors and consultants. So Sarah, I'd love it if you could share what I call your purpose, allow us to see the kind of professional meanderings that have led you to founding your company that have resulted in some of the books that you've pushed out into the world. That'd be very helpful if we get to hear that from you. SARAH: Yeah, and I appreciate it. I appreciate the word meander, too, because it definitely feels like a journey and it's kind of had some winding roads. So, again, my background and my kind of focus within the world of financial services is really around understanding client personality. And from an Odyssey or journey perspective, that really came from both the work that my father conducted over his lifetime. Tom Stanley was his name. He wrote The Millionaire Next Door. It was probably the most famous of the books that he wrote. But really, he was a researcher and really was focused on understanding affluent populations in the United States and how they became that way, and really found that there was one particular group. A group that was able to build wealth on their own. And I began working with him really when I was young. I remember him doing survey research and stacking up 15-16 page surveys on our dining room table and I did some sorting and things like that. Nothing too monumental. But later in my educational career, I went into what's called industrial psychology. It is kind of the application of psychology to the workplace. My specialty was in personnel selection and psychometrics, which is really the scientific measurement of characteristics, personality, attitudes, and all those things. And later in life with my father, he had a hard issue around 2008-2009 and I began to wonder how we could maybe use the insights that he had alongside psychometrics to anticipate what clients might be like to work with, anticipate which clients might be the Millionaire Next Door in the future, and also really understand clients. And so that's how I came to be in the financial services world. I was previously in HR. But I began to create these assessments alongside the work that I was doing with him on the Next Millionaire Next Door. And that's where we landed. I had worked for an HR tech company when I first came out of graduate school. And so I had that background in technology and watched a tech company go through multiple different stages, from being VC-backed to being acquired, and all those things that will go along with that. So I thought that maybe I could do that myself. Maybe not at that grand of a scale, but that's where DataPoints came from. It was really out of wanting to be able to provide these assessments to financial planners to use with their clients. JOHN A: I'm so grateful for the birth of DataPoints, and I really appreciate that description of this journey that you've been on, Sarah. And I think it's marvelous that toward the end of your dad's life, you were able to collaborate with him around The Next Millionaire Next Door. I have a copy of The Millionaire Next Door. I'm gonna race now to get my copy of The Next Millionaire Next Door. And we're so glad that you've made the transition from HR, which is an important contribution. And we all benefit from better human resource decisions and applications. But this kind of application, the practical use of assessments and statistics, I'm very interested in how that connects to the decisions which clients make around their saving, spending, and investing habits and behaviors. So can you help us understand how personality and attitudes may predict what those decisions around saving, spending, and investing might look like? SARAH: Yes. And part of that, again, grew out of my background in HR, which was to anticipate or predict who might be good at a job. So those were the kinds of folks that obviously organizations wanted to hire. But the truth is that personality predicts a whole lot about what we might do. And, again, we're applying it here to financial decisions. So as an example, we know that clients who tend to be very conscientious — those that show up on time for your meetings, those that are checking off their list of homework items they have to do rather quickly, those that pay attention to details — often those clients tend to be those that save more than others. So there are clear relationships between some of these characteristics that might sound familiar and outcomes, like investing decisions. So for example, we know that clients who tend to experience fear and anxiety and worry more than other clients — that's what we would call emotional stability or more of a psychological term would be neuroticism — tend to make decisions that are not often in their best interest when it comes to investing. “I feel this fear and anxiety, I want to get in control of that. So I'd make a decision. I might sell when I shouldn't.” That kind of thing in order to feel better. And so that's a personality characteristic that's important to know. So there are a whole host of different characteristics that predict different aspects of financial management. Our job at DataPoints is really to help advisors understand. You would certainly want to know everything about a client, but you only have so much time to get to know them and for them to complete an assessment. So we kind of narrow down what's important to know depending on the client type in the situation. JOHN A: I've always been fascinated by movies that feature like Hercule Poirot or Sherlock Holmes. And they have these keen powers of observation and they understand human personality. And I just loved that unfold on the screen. But the reality is, through your work at DataPoints, you're helping us find ways to anticipate what a client might do, what their next moves may be, by measuring personality. So I wonder if Holmes and Poirot would have been DataPoints subscribers [laughs]. They might have, Sarah. SARAH: Yeah, they might have put us out of business, right? [both laugh] JOHN A: No. I don't think so. I'm just gonna throw this in before I ask you to respond to the possibility that through these scientific tools, we can anticipate what a client might do by looking at their personality traits. But because DataPoints is growing, your influence is spreading within the financial services community — I love to see that taking place — but I wonder if someone who is listening to our podcast today has a thought about an area that they wish they could better measure or perhaps an application of the psychosocial dimensions (psychometrics as you call them) to the work that they're doing with their clients. Are you open Sarah to conversations with them where they might ask you, “Is this something that we can measure? Is this something that you could help us build?” Is that a conversation you'd be willing to have? SARAH: Yes, and interestingly enough, in the last (I would say) probably six to eight months, that's the conversation we're having more and more, especially with larger organizations. It is round really pinpointing what is critical for that organization, that firm, or that practice, to know about their client, and then talking about either custom assessments or tailoring some of our off-the-shelf content to make it appropriate and useful for that particular firm. So one of the questions that we’re asked on occasion is: Can you help me understand which clients will be great to work with? Well, that of course depends on your firm and the types of advisors that you have and the type of work that you're doing. So there's a lot of kind of, again, custom-type work with those types of projects. But it's certainly something that we do. We've done that in the past, as well. JOHN A: Well, I'm going to encourage our listeners today to race to the DataPoints website and look at the variety of tools that are accessible there through Sarah's work. But let me return now to our conversation. Help us understand, Sarah, how measuring personality through the tools that you've developed or developing might help a planner, an advisor, or consultant anticipate where a client is going next? SARAH: So, really the focus, especially when we first started DataPoints, was ensuring that our tests were essentially predictive of something that the client might do. Because, again, a personality test that just sort of describes who we are is helpful and useful, but we felt like there was a real application and also, again, anticipating what a client might do. And so over the course of several years, we studied what factors would actually predict something like savings rate. So one of our assessments that's based primarily on The Millionaire Next Door used often for coaching new clients (younger clients often) actually predicts whether a client will save and kind of the percentage that they'll save compared to those that score low, let's say on that same test. And so you can use the tests like that — we call it the building wealth test. There are others out there as well — to actually predict which clients are going to be savers versus spenders. With our risk tolerance assessment and then our partnership with Dr. John Grable and the Financial Planning Performance Lab out of the University of Georgia, we were able to take a really broad approach to measuring risk tolerance in a way that also allows us to anticipate which clients will buy, sell, or hold during a down market. So if you use a combination of characteristics — not just one thing because we are very unique and a lot of things contribute to how we make investing decisions — but if we can use a combination of characteristics, things like, “Am I an emotional investor? What's my confidence level? What preferences do I have?” That can help us to predict things like which clients will buy, sell or hold. So those are some of the things that we do at DataPoints, in addition to what I was mentioning earlier: describing clients, their attitudes, and which clients might (from a household perspective) be in disagreement about what their retirement looks like, or their attitudes about things. So that's sort of a different application. JOHN A: It's interesting how these applications stretch from younger clients to the potential retiree. It's kind of crossing a wide spectrum of age and maturity. Sarah, help us understand how we can measure personality, not only through the assessments and tests, but through observation and interviews. Do those also contribute in a meaningful way to being able to gather data that would help us measure personality and begin to perhaps be in a better position to predict how to best help a client? SARAH: Yeah, absolutely. I'm obviously very biased to DataPoints because we have a lot of tests. But there are a lot of different ways that you can measure a client's characteristics. And for those of you who have been working with a client for 20, 30, or 40 years maybe, you know a lot about them already. You've observed them during down markets. You're able to see the kind of how they react when maybe there's stress at home. You can kind of anticipate what they might do. And the trouble with observations is that — especially if we have a new client — unless we're willing to sort of put them through what we would do with leaders, where we would put them through an assessment center for three days, and kind of simulate what stressful situations might lead to certain behaviors from a financial perspective, which I would not recommend anyone do, we don't know a lot. We're not able to observe much about a brand new client. And so that's where some of these other tools might come into play, if you're trying to understand the client. Interviews can absolutely do that as well. We would strongly recommend, if you are going to use an interview process to measure characteristics about your client, that you're using a structured process. Again, same sorts of questions. You really nail down what you're trying to measure. Maybe you even add a rating scale to say, “Okay. I think that this client could really withstand, from a personality perspective, a lot of volatility.” Maybe they get a 3 (rating), other clients would get a 1 (rating). And that would depend on their answer. But that takes some time. And even when I was working with organizations trying to get them to structure their interview process to hire new leaders, it was really hard to get people to ask the same questions and rate folks accurately. That's a whole different animal, so to speak. And that was in a situation where there were legal ramifications for not doing so. You have to ask the same kinds of questions. So that's where tests can come in and be sort of neutral, if you will, to some extent unbiased but measuring client characteristics isn't a perfect science. But that's kind of where that comes in. And then, there are other ways of measuring things. I mentioned assessment centers, giving them some role playing exercises, things like that. But those are a little more invasive. And I think from a client-experience perspective, that probably wouldn't be the best way to go. JOHN A: As I listened to you, Sarah, and I'm thinking about the emergence of AI and the fear that many of us have — I personally don't fear it, because I think I'm going to be gone by the time [laughs], but it's coming rapidly, so maybe I should — but I think there's this concern about displacement professionally, that we're going to be obsoleted by AI in the kind of potential that AI might offer for self-direction by clients. I'm wondering though, whether DataPoints and skillfully using the tools that you're helping create and bring to the marketplace, if this isn't really a first line of defense that advisors and consultants should take advantage of to strengthen their role as an advisor and someone who can really help clients in a meaningful way. So how might you see DataPoints and the information that can be harvested through the various tools and assessments that you offer potentially contributing in a significant way to enriching the dialogue, which an advisor may have with their client, around money, wealth, decision-making, their attitudes towards money and wealth? How do you see that all tying together? SARAH: I definitely have some thoughts on AI, too that I'd love to share at some point. But the beauty, if you will, of measuring characteristics, whether you're doing that through an interview process or some other means, is that it allows you to then probe in a very structured way around certain areas that could potentially help the client understand themselves but then also, certainly the adviser understands the client more. So just going back to our example related to investing-related characteristics. If we knew that a client was, again, we would say scoring low or was a little more emotional when it came to investing, maybe made some quick decisions and those sorts of things, I might want to understand why. And so by kind of pointing at the report and using that as a first step, I can then start to ask some questions and increase the comfort I have around asking questions about past experiences with investing, or even adolescent or childhood experiences, watching their caregivers or parents experience things related to investing. And so we really feel like the tests themselves and the reports are really just the starting off point for a deeper conversation around these different factors. If you don't mind going back to your AI comment as well. With a test, I'm disclosing information to you. You're asking me questions and I'm responding. And then a report comes up and it gives my score, so to speak. The trick with AI is that I didn't necessarily maybe even agree to share that kind of information with you. If you go to my Facebook page, my Instagram page, or LinkedIn page, and you start scraping data, and you look at things, and all of a sudden, you know things about me or you have some insight to share with me or questions that sound a little too much. It's the ick factor. Do I want my financial planner walking through these kinds of insights they're getting from AI with me? Or would I feel more comfortable if I had taken an assessment, maybe answered a few questions, and then had that report come back? So that's kind of where I land with that. Again, I'm biased, but I feel like the beauty of tests is that the client is self-reporting what they believe and if we've made the test the best that we can, it's a pretty accurate picture of your client. JOHN A: Thank you for stepping into the AI world with that observation. And I think that really makes a lot of sense. Do you have any final summing up you'd like to do for our listeners today? SARAH: I appreciate the opportunity to chat with you about this. I think we would argue whether you use our tools or another tool, your homegrown tools, helping your clients understand themselves and why they do the things that they do with money will be extremely critical as you're trying to help them achieve their financial goals. I'm not quite sure how someone could do that without understanding what makes their clients tick. And so, whether you work with us or use some other kind of tool, we really feel like that's the future of where financial planning is heading. It is really understanding clients at a deeper level. JOHN A: Sarah, let me be bold. We don't generally do these in our podcast, but I just really feel there's a reason that I should ask you to do this. What's the best way that a member of our community, a listener of this podcast, might begin to explore what DataPoints offers and how do they best engage with you? SARAH: Yeah, absolutely. On our website, we have a couple of different tests that advisors can take. They can even have their clients take them if they want. They're free to everyone. They're out there. You go to datapoints.com/personality and take our personality assessment. That's one way. And certainly we have a free trial and things like that, that are standard in the world of FinTech. But that’s really the best way: to take an assessment, to read through the report, especially if you do a trial to look at the advisor insights that are given, and decide if that's something that you would be comfortable having a conversation around with your client. That's really what we recommend when folks begin to work with us. JOHN A: That makes a lot of sense. Thank you, Sarah, for everything you've done, for being with us today and everything you're going to do. I really look forward to seeing where this journey goes from this point forward. Thank you. SARAH: Thank you so much.

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