- New client acquisition
- New and existing client sales
- Existing client advice and supervision
- Client retention
Gavin Spitzner: (
Hello there invest connect. I'm Gavin Spitzner. I head up a boutique strategy consulting firm called wealth consulting partners, where we live at the intersection of business strategy, wealth tech, and client experiences, all the things that we're talking about today at the, at the event. So I, jumped at the chance to moderate this conversation on a topic so near and dear to my heart: analytics, new ways of using client data, especially with this esteemed group of panelists, including one of the largest asset managers in the world, one of the largest RIAs in the country, one of the largest platform technology companies we've got with us Koley Corte SVP global head of transformation at AllianceBernstein, Farouk Ferchichi, chief data analytics officer for Envestnet and Rob Ziliak, chief experience officer for Buckingham Wealth Partners. We're tight on time. We got half an hour, uh, just a big time.
Gavin Spitzner: (
We could spend a couple days on it. So I wanna dive right into the only framing I'll do is I, I guess my alternate title, as, as I was thinking about this was big data, little insights. By that I mean, in real life, there's no magic in the data. It's, it's how we surface that in ways that that really improves how we connect with clients and a regular basis, all those little insights add up over time, that nudges that we can give them can have a real impact over time. So excited to get into this, feel free to throw questions our way throughout the session. We'll get to all that we possibly can. Um, with that, I wanna have the panelists introduce themselves, tell you a little bit more about themselves, their firm, their role, especially as it relates to the, the topic. Uh, and then we'll dive in. So Koley, if I could start with you.
Koley Corte: (
Sure. Thanks. Gavin. I'm Koley Corte, as Gavin mentioned, I'm the global head of business transformation at AllianceBernstein. And what I focus on is how we modernize our, uh, relationship with our customers, how we modern distribution, how we use that data that Gavin was talking about to understand our customers better, to develop new solutions that better meet their needs and to use that data to better engage them, uh, both digitally and through traditional channels, as well as to use digital, to extend our, the, our reach to our customers. Uh, I've been at the firm about four years. I think this week is actually my anniversary. Uh, and, um, before that I was actually outside of the industry. So I got some experience in the trade show business, uh, where we were really being disrupted or took the opportunity to disrupt ourselves, leveraging digital for a whole new way of doing business. Uh, and previous to that, I had been closer to this industry at TIAA and at AIG. So, um, thank you. I'm excited for this conversation. I think it's, um, big data and there's sometimes big insights, but I agree with you. It's, it's how we walk the, the walk and how we can use, put that data to work every day to improve how we work with our customers.
Gavin Spitzner: (
Awesome. Welcome Koley. Farouk.
Farouk Ferchichi: (
Thanks, Kevin. And, uh, happy anniversary, Koley. Uh, I'm Farouk Ferchichi,, like Gavin said, um, chief data officer for Envestnet, which is among other, uh, companies, uh, has Yodlee in its portfolio of companies that we manage our mission, uh, at, in Envestnet is very noble. Uh, from my perspective, at least is to enable intelligent life for everyone. And we do that by orchestrating what, what we consider the financial wellness marketplace, uh, using data technology, obviously we're in a journey, uh, of migrating away from, uh, or transitioning away from a company that was TAMP centric, uh, to recently a more unified wealth platform company, uh, to more, uh, tech company, uh, as we look to the present and the future, and to become a truly tech company kind of data analytics, uh, transformation is at the, um, forefront of that growth strategy that we have in our, uh, overall business plan.
Gavin Spitzner: (
Excellent. Welcome Farouk. Rob, Chief Experience Officer. Love that title. It's all about client experiences. Welcome.
Rob Ziliak: (
Thank you, Gavin. Uh, yeah, chief experience officer, most people follow that up with the question of, so what does that mean? Uh, I have the great privilege of being able to empower advisors, uh, to help clients design build and protect financial lives. So Buckingham wealth partners is a fee only fiduciary firm. Uh, we have two major lines of business. Buckingham's strategic wealth is the RIA, we have about 26 billion of assets under management. Uh, and we also have Buckingham strategic partners, uh, a turnkey wealth management platform that provides back office solutions to hundreds of smaller advisory firms. And that represents another 40 billion dollars of AUM. So we work in a very, very highly personalized business, uh, very much concierge level relationships between advisors and clients, but yet data is necessary for us, uh, in order to obtain it, organize it and utilize it in ways to help advisors gain scale, because we know the landscape is continuously changing in our profession and we need to better utilize information so we can actually even further personalize relationships.
Gavin Spitzner: (
Fantastic. Well, welcome to all of you. Koley, maybe I can start with you. So none of the, the really interesting things we can do, uh, with, with data happens without good data, right? Garbage in garbage out, GIGO. We have vast amounts of data from our CRMs. Two books of record, financial planning systems, portfolio management systems. Maybe I could ask you to lay the foundation for the audience. Um, as my friend Alon Davidovich, uh, at Edward Jones spoke about earlier today, there's really five things we do with, with data. We capture it, organize it, analyze it, distribute it throughout the organization. And then ultimately where it really matters is we take action on that. So how, how do we surface those insights act on the intelligence we uncover within the data? So Koley, if I could ask you to talk about that foundation and some of the things you've done at AllianceBernstein to get good clean data and, and, and work with it.
Koley Corte: (
Great. Uh, yeah. Fact I was on the panel with Alon, not that long ago. So, um, he, and I see the world very similarly in that light. So, uh, we are in the midst of a project that we're calling Oculus. And the idea of Oculus is to enable a, a single view of the customer for our sales teams, so that we can get a view of all our customer data. So we can manage that primary data that we're creating around our customers and we can pair it with all those other data sources you talked about. So can we bring in industry data, can we bring in the data packs from our, our partners, um, and align all of that, so that then we can create a series of signals that we can service to the sales team that lets them know where are their growth opportunities with existing customers, where are their new high, high value opportunities to engage?
Koley Corte: (
Some of that's using, um, some of our partners here, data to, to surface, right, and their algorithms. And then additionally, where can we also, uh, bring in, you know, third party, uh, digital data and other things that can help us inform our next action. So it's really a sense of what's the tempo we should engage a customer based on that customer, own preferences and style. What is a growth opportunity for us who are new customers we can engage and where are their customers at risk. And now harnessing all that data. We can surface that as well as drill in and get a full view of the customer and all their touches with us, how engaged they are. And that will also give us a profile of where there's opportunity or risk. So, um, that's a, that's a bit a undertaking for us. It sounds like it's like super easy though.
Koley Corte: (
We've brought all that data together and we're just gonna bubble it up. It's been, uh, uh, an arduous journey, this tool launching on, on December 6th. Uh, um, there's still hard work in the next couple weeks. We're in the midst of user testing, but we're super excited about really putting that data in people's hands in a way that they can take action much more easily. People are taking action with the data today, but they're using a silo view. Uh, someone's using who was on my last webinar, another person's using what is, uh, Envestnet tell me, I should do someone else is like, oh, I have a list from here. Right? But this way we can get to the universal best actions that fit for that customer and surface them using data science, uh, and really allow our people to be superheroes, right. So they can be right place at the right time as effortlessly as possible.
Gavin Spitzner: (
Love it. So Farouk, uh, totally kind of teed you up right there. You're at the center of a lot of data initiatives. You own a lot of the data, or you, you aggregate a lot of the data through different mechanisms. Why don't you share some of your perspectives around that, that monumental task to organize the data collected disseminate it?
Farouk Ferchichi: (
Yeah, absolutely. Yes. To everything Koley said, , that's a short answer. In fact, you know, as Envestnet, we understood kinda the, uh, the need that Koley, uh, was sharing, uh, across many, uh, wealth companies and other other companies across the financial wellness industry. And we've developed kind of a data vision simply to kind of build a data intelligence platform where we we wanna Koley and Rob, and like executives in these companies to spend most of their time using the insights to run their business, rather than, than figuring out how to make the sausage , which is not an easy task as maybe Rob and colleagues and, uh, can attest to. So we, we, we are in the process of bringing to market this plug and play data intelligence platform. The plug part is how can we to Koley's, um, uh, have customers connect all their data from the different silos, uh, residing in our investment platform, but outside as well in whatever systems they have, then the play part, uh, immediately after they connect all that data, um, start using out of the box BI insight solution that are proven to work also a next best action engine, uh, powered by our recommendation engine, uh, proprietary solutions as well as be able to kinda, uh, govern the data as it's an emergent topic about what's the dictionary of your data, what it means and all of that, and also give a, the data scientists a development environment that will allow them to spend less wrangling the data and organizing the data and building that one version of the truth and actually spend 80 percent of the time building those algorithm and insights and next generation model that, that, that folks like, uh, Koley and Rob and others, uh, business executive can actually act on to, uh, to drive the growth of the business.
Farouk Ferchichi: (
I can go on on about this platform.
Gavin Spitzner: (
I'm glad you you've brought in the lens of the, the, the business and, and management, because we're focused a little bit more here on, on advisors and, and client insights, but I'll tell you, in my, my travels with, with enterprise clients, I'm convinced most of the executive team is spending 30 to 40% of their time, wrangling data, pulling it from here, here, here, it in together, a report. So I think that is a, that's a massive, uh, benefit is, is that we probably don't spend enough time on is, is how do we streamline that provide better business intelligence, not just at the advisor and client level, but the, but the enterprise level. Um, Rob, I wanna turn to you, and I've got some more questions for you, but maybe just on this topic first you're in the middle of a lot of these initiatives, just in terms of how you're thinking about organizing data, collecting data, any, any other perspectives you'd add to what Koley and Farouk shared?
Rob Ziliak: (
Yeah, I I'll build on what they shared and not contradict any of it. So, uh, we are absolutely looking to, to establish, uh, dashboards that help the leaders of our firm, uh, dashboards that help advisors manage their client relationships and parlay what we learn from those to build better end experiences for our clients and prospects alike when they're engaging with our technology. One of the ways, Gavin, that we are most fixated on trying to utilize data for the benefit of advisors and clients alike is built on Dr. Moira Sommer's book, Advice That Sticks and her research showed that 80% of advice given in our profession goes unimplemented by our clients. Um, what a crying shame that is and what a terrible expense that is because we have clients paying for that advice, but then not benefiting from it. So what we're trying to utilize is, and what we have done at Buckingham is build a number of internal workflow into our, our CRM.
Rob Ziliak: (
Uh, so that advisors have preset or prepopulated opportunities to advise clients across the spectrum of their financial lives. So whether it be estate planning, annual tax planning, investment planning, uh, risk profiling, whatever the topic is, is in the spectrum of finance, we've actually established pre-populated and pre-defined timelines in which advisors can utilize each of those at a given client relationship and what, what the advisors still have the latitude to do is change when they deliver any and form of advice to the client, but it's at the click of a mouse within the CRM. So we're really looking to use that data about client relationships, to empower the advisors, to give it a more personalized and scalable, uh, service offering to the end client.
Gavin Spitzner: (
That's fantastic. And we'll, we'll dig into the, add some more Farouk, maybe I'll, I'll come back to you for, for a second. You know, again, you're, you're sitting on this mountain of, of data. You're obviously in the middle of a lot of firms, data initiatives you've talked. Um, I think it's one of your most recent investor decks about generating millions of predictions already on route to, more than a billion the next few years. Can you talk a little bit more about how you're responding to, to firms like these, their data needs their challenges, um, and maybe a few more specifics around predictive analytics? I think that's, that's one of the, the parts of this that the audience is most interested in. What are some of the use cases? What, what, what are you seeing? What are you working on?
Farouk Ferchichi: (
Yeah, absolutely. And much like Rob, and when, when he, uh, spoke about the opportunity and surface out to the advisor, it's the same line of thoughts. Just gonna go a little bit deeper into it, uh, per your question. Gavin is one, let me, let me kind of reinforce what, why we, we are bold and have the vision that we have and what we've communicated to the investors, because we truly have a unique data set. We have over 17,000 data sources as a company. It includes 5.2 trillion, uh, of, of, of managed asset information in our platforms, uh, representing over 14 million investors serving over a a hundred thousand advisors. All of that coupled with the Yodlee data of over 30 million users, 500 million linked accounts to name a few stats, that's pretty big data set that allows us to further analyze. And as a byproduct of that, we kind of built a recommendation engine or an opportunity engine, which started out initially as a pure analytical, uh, tool and exercise.
Farouk Ferchichi: (
Uh, we applied both determined and probabilistic models, probabilistic in the sense of deterministic in the sense of rule based, uh, what we know to be for sure, probabilistic in the sense of predictive and prescriptive at the same time using AI and machine learning model to drive a simple outcome, which is a next best action for the advisors at the right time, at the right place to advise their clients. So we're kind of the behind the scene, helping the advisor in three different ways. Uh, one recommendations, we broke it into three categories. One is productivity type of, uh, recommendation where, for example, using complex life event models, we can predict the next life event of a client, which helps advisor obviously prioritize that contact strategy and accordingly personalize that message for the client. The second category of recommendations that we built is, uh, conversion type, for example, advisor can prioritize solution offering to current clients like, uh, adopting the tax overlay these days to help them maximize their return on their assets.
Farouk Ferchichi: (
Another third category of recommendation that we've worked on, we continue to evolve is around optimization. For example, uh, an example of this category is we provide the, uh, recommendation for the clients for their next best action to consolidate their held away assets, not currently with advisor to consolidate with the advisor, by providing the upside of moving into the portfolio of the advisor that has already proven through conversion and productivity, that they are the best partner for the financial, uh, portfolio of that particular client. I think where we will go and Gavin, uh, is taking recommendation engine to the next level. Uh, and that's really by turning it into, uh, an advisor engagement tool, which is part of that data intelligence platform that we talked about earlier. Uh, basically it's not enough to build the insights and make them simple to use by the advisor. But I think like Rob said earlier, embedded in their daily workflow using a push and pull method rather than waiting for them to consume that data.
Gavin Spitzner: (
Yeah. And, and something you touched on there around predictive analytics, struck a chord in the sense of people like they wanna know, and this is where advisors can add so much value. They wanna know people like me that have, or in similar circumstances, what are the things that I should be doing that, that I'm not maybe over the next few years. Um, and Rob, I know you're, you, you're doing a lot of work around in terms of journey mapping and helping lay out those different milestones. Koley, let me pull you back in here. Um, right. You know, a lot of different jumping off points, but in the sense of predictive analytics, um, uh, thinking about things like next best actions, can, can you share some of the things that, that you're either applying in your, in your business today or, or working on in the future?
Koley Corte: (
Yeah. And so there's a couple things we're doing, and we, we have an institutional business which works with institutional asset managers directly. And then we have an intermediate business where firms like Rob's are customers, right? So we try to understand the home office and the advisors so that we can really deliver to them and they can deliver to their own customers, uh, in terms of being predictive. I mean, we do, we do leverage some things straight outta the box from partners like Farouk, where we are using some of their algorithms, uh, to understand, you know, probable bias as an example. Uh, but then we, we like to combine that with our own data so that we can then prioritize that against other actions that we could take. Right. And other other ways we can act with our customers. So, uh, some things we do are using, um, purely third party data elements, like, uh, scraping websites, understanding recent news understanding events, and then thinking about how can we get a jump and take action faster to, to be, you know, aligned to the customer and their need.
Koley Corte: (
And in other cases, it's based on the customer's behaviors indicating to us that, you know, there there's a need or a risk. Um, and so we've built, um, uh, a variety of sort of probabilistic outlooks, and then some things are just rules based, right. Um, we'd like to get those rules to be more sophisticated over time as, as we see the impact of them. So let's say, we say a customer like Rob, we should be touching every 90 days. And then we learn, well, are we actually able to engage with Rob and I, every 90 days? Does, does Rob tell us no you're calling too frequently, or I'd like to see more, I need more insights around this, right? We're, we're gonna capture all of that and then use that to then customize much more of that, um, pacing or tempo in which we engage the customer as well
Gavin Spitzner: (
And engagement. I'm glad you, you're using that word for me. This is as much about the engagement, um, best practices as it is about the insights and what we can learn. And there's some really great use cases in terms of machine learning and, and, and, you know, understanding what to Rob to your point about that 80% stat, uh, not, not actually acting on the advice so much of that is how we engage. What, what are the right mechanisms? What's, what's the right frequency? How do we personalize that advice more where they feel like this is really about me in my situation, Rob, you wanna share some, some other things that you're thinking about and working on it at Buckingham in that regard?
Rob Ziliak: (
For certain, uh, I'll, I'll touch on a couple of different areas. Gavin. So, uh, one of them is kind of old school data. If you will. We, we do conduct annual net promoters score surveys to understand, uh, what our clients think of our client experience and data from that has told us two things. One is that they're extremely happy with their overall fiduciary relationship. So we can feel good about that. To the constructive side, though, it's helped us launch, uh, new service offerings. So within past 24 months, the voice of the client data has driven us to launch cash management offering we've, uh, established a new tax planning software relationship to do more proactive tax planning, work for clients. We've recently established tax prep and filing services underneath our umbrella. And, and also our newest one is business valuation and succession planning for business owners.
Rob Ziliak: (
So we're very much using the voice of the client to help us determine what are new service offerings that can add even more value into their life. Uh, a second aspect is that, um, we're, we're looking at engagement to help determine to what extent do our clients want financial education, communication, uh, frequency and format alike. And when we talk about our clients, we're actually talking about advisors too. So the days of just blasting out emails or newsletters, uh, or maybe a video here or their no longer feels good enough because we can rely on data for the recipients to tell us, um, even implicitly to tell us whether or not what we're sharing with them is actually value added. And if it's not, we can pivot accordingly.
Gavin Spitzner: (
I think that that's a great point and I'm seeing some really great capabilities out there on the market now that that apply or allow the advisor to apply their, their insights more readily into those types of things. You know, this is, this is useful, this isn't useful. And then machine learning too, start to weave that into, into the algorithm, to tee up things in the format, to your point, um, and the content that actually matters. And in so much of that is it's that it's an overused term, but hyperpersonalization both at the client level and the advisor level, right? Cuz not every, advisor's the same in terms of the practices that they run, the clients that they serve. So the more personalized we can tee up that content and make it easy for them to act on it, take that friction out of, uh, here are the insights now, what do we do about it? Whether that's something about likelihood to, to a trip. I know, you know, that those have been a lot of the early use cases, uh, for, I know Envestnet has focused around that. Can we look at the data and see where do we, where might we have a retention issue? Um, put that data in the advisor's hands in a way that's really useful to them. Is, is it from an, maybe from an advisor standpoint Farouk anything else you wanna share around, around those types of initiatives?
Farouk Ferchichi: (
Yeah, I think, I think building on what Rob said, uh, the, the closed loop feedback. So there's one thing of building a rich data set on our side to better understand the client and the advisor, uh, and develop, better, you know, recommendation and give it to them at the right time, right place so they can act on with their clients, but that closed loop feedback. We gotta crack that. And it sounds like Rob has it figured out, but from our perspective where we are trying to mix up the data that we serve to our advisors through the enterprises and the RIAs, and as well as getting that feedback, uh, it only strengthens the quality of these recommendations and have them be more adopted, uh, by the advisor, you know, earlier, uh, Gavin asked me question about, I made a promise publicly in the investment day that will be producing a billion, uh, recommendation over a billion recommendation while on the surface, it sounds like a lot.
Farouk Ferchichi: (
Uh, let me kinda share with you the, the, you know, the basic math behind it, which will immediately, I hope will, will make Koley, Rob, and you, and hopefully the audience says no, no billion dollars, this is really an undervaluation of what this could be. Um, if you think about it, the number of clients in question, right on one hand, one dimension, and then you add on the other end, the number of advisor in question that you're trying to optimize the recommendations for, then you multiply that by the number of wellness opportunities across better spending, better saving, better protection insurance, better investment decision, which is a lot of attributes there. Then you finally multiply that by the possibilities of what is the best match finance product insurance, product wealth product. Now, if you move to apply that four by four matrix, that is, uh, shared. And honestly, if you do a simple match, that's hundreds of billions of recommendation over a period of five years, if not in the trillion. So my billion-dollar promise was really, uh, uh, a tip of the iceberg that I don't wanna scare people, but the reality is there's so much opportunity in this space to give the advisors what they need when, when they need it actually .
Gavin Spitzner: (
Right in the service of their clients. And, and
Farouk Ferchichi: (
Hundred percent
Gavin Spitzner: (
The, you know, I look at so much to the early of sudden next best action was, was very product oriented. What's the next product we can sell. Um, I feel like we've evolved as an industry quite a bit, uh, past that too, whether it's next best engagement, that's a term I'm, , I'm using more, um, and really, you know, serving the client, presenting these ideas in a way that they, that resonates and helps them see how this is gonna improve their, their financial life. Uh, Koley, we're down in the last couple minutes. Let me, uh, yeah. And I just
Koley Corte: (
Gonna add a point on that, um, because you remember I mentioned earlier that I was outside of the industry before, and one of the things we really learned was in the trade show business. When you register to go to an event, you talk about what your interests are and you tell people why you're coming to the event, right? And then we were trying to surface recommendations much like a next best action type idea, right? You're at the event, here are things that here's events, you know, uh, conference things that you should go to here's booths you might wanna stop by. Here's an offer. And what we found is it's important to understand what the customer is asking for to Rob' point, but it's also really important to observe the or behavior, right, because what they say they're gonna do and what they actually do. And I think this is incredibly important to financial awareness, financial education, financial, um, engagement is, it's not just what you say, it's actually how you engage.
Koley Corte: (
So I think it's a combination of, for us, what the salesperson thinks, what the customer says, and then watching the behavior. But that behavioral element is like probably critical in informing these algorithms. And I think that's could be the secret sauce to unlocking, you know, what, what is real right? If you think about what Amazon's recommending to you, they're recommending it. And Netflix is recommending to you, they're recommending based on what you've done and what people like you have done. And if we can do it based on behavior, as opposed to what people tell us or what the data just says, that's really critical. So, sorry, I've been trying to jump in.
Gavin Spitzner: (
Point. No, that's, that's a great point. And I love getting folks from outside the industry in cuz you've got so many insights, Amazon's a good example too, of to your point about the behavior. It's what you do, not what you say. It's looking at, you know, search activity and all the things. I was with the company in Cambridge last week. The, you, you, you don't wanna know what, what people know on an anonymized basis about all of us, um, looking at all that, that activity. Well, unfortunately we gotta, we gotta wrap here, uh, coli Faru Rob, thank you so much for your insights for generously sharing with your colleagues in the industry on this. We'll love to do a follow up, uh, down the road and, and see how all these initiatives are, are turning out. But thank you all very much.
Koley Corte: (
Thank you. Thank you.