The Latest In Financial #AdvisorTech (March 2024)

Datalign Generates $15B In Advisor Leads As Paid Lead Generation Services Gain Advisor Momentum

It's expensive to grow the client base of an advisory business. Whether it's the cost to run a marketing event, or to buy a "stack" of leads (Glengarry Glen Ross style!), or the opportunity cost of the advisor's time to attend a networking meeting, getting clients has a very real cost. For which, in practice, most advisors simply have to make the personal decision, based on their own available resources, about whether they're going to take a time-based approach to marketing, or (if they even have the financial capital) front the dollars in the hopes of getting to growth results a little faster (if they're newer and don't have an established personal brand/network in their market) or cheaper (if they're more experienced and the cost of their time is getting expensive).

Over the past 20 years, with the rise of the internet, a lot of advisor marketing has gone digital. But the core remains the same: advisors can do it with their time (e.g., blogging and podcasting, or 'networking' via social media), or they can do it with their dollars, with the rise of various lead-generation platforms where advisors pay to be listed in a directory (e.g., FeeOnlyNetwork, Paladin Registry), pay for a list of leads to call upon (e.g., SmartAsset, Ramsey SmartVestor), or outright pay a solicitor fee for introductions that close (e.g., Zoe Financial).

All of which provide financial advisors with a good range of marketing options across the spectrum of needs. Newer financial advisors tend to start with time-based tactics because their time is cheap when they don't have much revenue or clients to service yet, though startup advisors with some financial capital may quick-start themselves by buying leads to get to their first clients more quickly. On the other hand, experienced advisors often bottleneck around time-based tactics as their time becomes expensive (higher opportunity cost at higher levels of client revenue), and more are shifting to include some dollar-based tactics to scale beyond their personal time constraints.

But ultimately, the biggest driving factor of which type of advisor marketing is best is simply whichever has the lowest Client Acquisition Cost. Is it cheaper to spend the advisor's time – at whatever the cost of that time is for a particular advisor – or can a third-party marketing firm scale itself to the point that it can bring in leads at a lower cost (such that it can charge advisors for those leads, have it still be cost-effective for the advisor, and can make a profit)? For instance, if an experienced advisor averages more than $4,000 in acquisition costs for a single new client (per Kitces Research on Advisor Marketing), a marketing platform that brings in leads for $3,000 itself can sell those leads to advisors for $3,500 (making a $500 profit and saving the advisor $500 as well).

Which may challenging, though, because third-party marketing and lead generation platforms can focus all their time and resources on efficient lead generation… but they have to figure out how to scale up to support dozens or even hundreds of advisors who all want to buy leads. Which can quickly become very capital-intensive for lead-generation providers. And faces the risk that, like most marketing tactics, there will eventually be scalability problems where channel being used to generate leads faces rising costs for diminishing returns, breaking the economics of the offering (unless the firm can keep finding new scalable marketing channels).

And so it's notable that this month, paid lead generation provider Datalign Advisory announced that it is still very much seeing an acceleration of lead flow, having referred almost $15B of prospects to advisors in 2023 (and advisors having closed almost $3B of those in actual new assets converted) in just its second year.

Functionally, Datalign Advisory is in the 'pay-per-lead' framework, most similar to SmartAsset's SmartAdvisor program. Though Datalign is a bit different, in that advisors bid for the lead in an auction-style system (based on how well it meets their firms' parameters) instead of SmartAsset's standard-fee-based-on-asset-size, and the lead is only sold once (to the highest bidder) instead of SmartAsset's system of providing each prospect name to 3 advisors. Which can potentially be more expensive for advisors to get each lead, but also less competive if they're the only responder… which helps to explain why Datalign reports that almost $3B out of nearly $15B of referred assets have been converted, implying a 20% conversion rate. (Which means even if an advisor pays $1,000 for a single $500k prospect, it can be a good deal if the advisor gets 1-in-5 of those prospects as an actual client, effectively generating $5k of new revenue for a $5k marketing spend.)

From the advisor's perspective, the first question is simply whether the advisor wants to spend on paid lead generation in the first place, as, at best, it's still a numbers game that takes a higher volume of "no's" to get to a single "yes." Though arguably it's more scalable (easier for the advisor to turn up the spend if they want to grow faster, in a world where they can't just turn up their marketng time), and the cash upfront can still be cheaper than an experienced advisor's expensive time. However, it also still takes time to chase down leads, and meet with all the not-ideal-fit prospects to eventually convert a few 'strangers' from the internet. So to each their own.

Still, Datalign's higher conversion rates than other lead gen systems may be particularly appealing to advisors who really dislike needing to filter through low-quality leads. Especially since, as Kitces Research has found, most advisors weigh quality-of-lead over volume-of-leads and even their overall return-on-lead-investment (i.e., advisors would rather pay more for fewer high-quality leads, than pay less for a high volume of leads with a better financial outcome that requires wading through more "no's" to get to that result). Which means Datalign's system may be more expensive per each lead, but still more rewarding for the advisor (and can still be a solid ROI, too, if the advisor can find the right-fit leads at the right price).

From the industry perspective, though, the meta question is how many lead generation platforms can be supported at once in a competitive marketplace, and what the total lead generation opportunity is before we just run out of consumers looking for a financial advisor. For instance, Datalign has been partnering with media companies, similar to SmartAsset, because that's where a lot of people/eyeballs are. But once the 2 of them have partnered with 'every' sizable media property there is… where will the growth come from next?

Secondarily, the added irony is that to make third-party lead generation platforms work, advisors still need the capacity to 'chase' the leads (to the necessary follow-up to nurture them through the sales process). Which tends to be difficult for smaller/solo advisors who might be tied up in client meetings when the lead comes in, and is easier for larger firms that can hire full-time business development associates… which 'arguably' aren't the firms most in need of growth support anyway, and raises the question of whether paid lead generation platforms become a pathway where the biggest firms just get even bigger as they outspend and outbid smaller firms (who then have to niche specialize to be able to attract enough prospects to survive?).

Nonetheless, growing momentum for Datalign and their lead-gen ilk suggest that more advisory firms are realizing there really is a good ROI to spend cash for prospect leads. After all, even a $5k spend for just a single $500k client is ultimately 'just' a 1:1 cost:revenue spend, as compared to industry mergers and acquisitions that are often 2X or even 2.5X revenue. So even as advisor marketing is getting more cash-intensive than it was, the irony is that it's still a much better financial deal than the industry's M&A focus? Which bodes well for Datalign and others… at least, if they can figure out how to keep scaling up the volume of leads to meet the rising demand?

Salesforce Financial Services Cloud Aims To Come 'Downmarket' To Mid-Sized RIAs With Pre-Built Multi-Custodial Data Feeds Powered By BridgeFT

In their early days of computers, Customer Relationship Management (CRM) systems were little more than a digitized version of a rolodex, capturing the contact information of people the firm had done business with in the past (or perhaps prospects the firm hoped to do business with in the future). More sophisticated CRM systems might have a wider range of fields to capture more data points about the contact (e.g., their hobbies and interests) or where they are in the sales process (e.g., sales pipeline reporting), or more of the communication history with them (e.g., a series of call or meeting notes), but in the end each customer record was simply a flat file of data about that individual.

Then came Salesforce. Nominally it was 'just another CRM system', but Salesforce took a particular focus from its earliest days in solving CRM problems at enterprise levels of scale… where ultimately, it's about far more than 'just' the customer contact data. It's also about business workflows, integrating with other enterprise data sources, and being able to support the increasingly specialized needs of each individual enterprise. Which Salesforce brilliantly facilitated with a combination of a platform chassis that could support its own ecosystem of developers, and the rollout of its AppExchange that made it even easier for developers to build their own custom applications to support specialized needs of certain segments of Salesforce enterprise customers.

The caveat, though, is that Salesforce's ability to support the complex needs of large enterprises also made it a system that was very expensive and cumbersome to support for any company that was not a sizable enterprise with significant resources to allocate to its CRM systems. Which helps to explain why, in the independent financial advisor world, Salesforce has a >50% market share amongst advisory firms with 10+ advisors, but barely 12% of advisory firms with no more than 3 advisors (according to the latest Kitces AdvisorTech Research). And while this is arguably a great success for Salesforce in penetrating its core enterprise market, it's also problematic in the financial advisor industry, where the overwhelming majority of advisory firms are on the smaller end of this spectrum. Such that in the aggregate, Salesforce only has a market share of about 17% of independent advisors, and most smaller advisory firms don't even use Salesforce "out of the box" and instead purchase a pre-built advisor-specific overlay like XLR8, PractiFi, or Skience.

In an effort to further bridge this gap, back in 2016 Salesforce launched its own pre-built overlay, "Financial Services Cloud" offering, in an effort to deliver a more viable out-of-the-box solution directly from Salesforce, while still allowing advisory firms the flexibility to further customize to their specifications with their own Salesforce instance (which can be more challenging with third-party overlays that provide but tend to require that advisors use their own pre-built framework).

And now, the news is out that Salesforce Financial Services Cloud (FSC) has helped to develop a new Salesforce AppExchange solution called Attune, providing pre-built multi-custodial data feeds that can plug directly into FSC (powered by BridgeFT's custodial data feed APIs).

From the advisor perspective, BridgeFT's Attune plug-in for Salesforce FSC should make FSC a more appealing consideration for the upper end of mid-sized RIAs in particular (e.g., from $500M up to $2B of AUM, which might have 10 – 50 advisor professionals), which are both more likely to need multi-custodial data feeds (as advisory firms often add a second custodian as they grow), and may prefer the greater flexibility of FSC to customize more specifically to their needs as the firm grows.

From the industry perspective, Salesforce's efforts to move into mid-sized independent firms is a 'downmarket' shift from their traditional large-enterprise base, but arguably moves into what is still a potential sweet spot for Salesforce, with firms that need more depth of customizations and enterprise-level workflow and integration capabilities than Wealthbox or Redtail (which dominate amongst smaller firms), but aren't large enough to go the 'traditional' Salesforce buy-and-customize route.

In the long term, though, Salesforce's roots are still in solving the needs and complexity of enterprises… at a price point that enterprises can afford (but the long tail of smaller firms cannot). Which means even as Salesforce comes incrementally downmarket into the larger end of mid-sized firms, they do not likely present a substantial threat to the existing CRM providers that serve small-to-mid-market advisors. Instead, arguably, the real question is whether existing CRM providers for advisors can continue to expand their capabilities to move 'upmarket' and compete more directly with Salesforce… or if the ongoing progession of advisory firms as they grow will continue to 'force' them to make a CRM change when they reach the "enterprise" stage?

SEI Invests $10M In TIFIN To Incubate AI Applications For Wealth Management

Change comes very, very slowly to the world of financial advisors. In part, this is simply because financial services is a highly regulated industry, which creates barriers that slow the pace of adoption. It's also because financial advisors themselves don't make changes very often, as Kitces AdvisorTech Research shows that the typical advisor holds an average piece of technology for upwards of 15 years(!). And in the end, when advisory firms often tout retention rates as high as 95% to 97% - which means the average client stays for 20-30 years at a time – there's very little impetus for advisors to change quickly. Because the reality is that even if an advisor does nothing and changes nothing to "keep up with the times", there's little material risk that it will impact their revenue retention at all. Thus why even though the internet showed up in the late 1990s, a lot of financial services firms didn't get to the cloud until the early 2010s. And while robo-advisors and their 'next generation' digital onboarding showed up in the early 2010s, a lot of financial services firms didn't manage to digitize their onboarding until the early 2020s (and a few still aren't there yet!).

At the same time, while financial services industry pundits may overestimate the pace of (or need) for change in the short term, it's crucial not to underestimate compounding change in the long run. After all, 20 years ago, RIAs were a bunch of pioneers in the proverbial wilderness, with little technology or infrastructure support; the dominant players were the wirehouses, which could only find growth from one another and made the Broker Protocol to manage the intra-wirehouse poaching. Yet now, RIAs are crossing wirehouses in total market share, and wirehouses are pulling out of Broker Protocol because of how many advisors they're losing to the RIA channel… a 1% - 2%/year market share change each year may not be much, but when it occurs persistently, it can still be transformative over time!

This dynamic can be especially paralyzing for a lot of financial services firms, that suffer from the classic Innovator's Dilemma. The challenge is that for large firms, it's often just not cost-effective to try out new offerings in new markets, because even a high growth rate does little to move the needle of a mega-firm relative to its core business (and even less if it's a "given" that adoption will be really slow, even if it's going to work out in the long run). Yet waiting too long on change can lead to disruption in the long run, if by the time the firm realizes the "big new thing" really is compounding against them, it may be growing with too much momentum to stop it.

Which raises the question of how else big firms can stay "close" to the innovation, without getting stuck in the politics of their own firms not wanting to invest into "slow growth" new projects relative to focusing on their profitable core?

And so it's notable that this month, the news hit that investment outsourcing giant SEI is investing $10M into TIFIN, with a particular focus on supporting the development of its AI offerings (which can then be tested with SEI's advisors).

To the extent that this is how SEI wants to "stay close" to innovation, the deal makes sense. SEI has the reach in the sheer number of advisors it serves, across a wide range of both broker-dealer and RIA channels, which gives SEI a lot of room to pilot new TIFIN offerings to SEI's advisor segments. If they're successful, SEI can then evaluate whether to acquire more or all of the TIFIN offerings to bring them in-house, or let TIFIN sell the solutions to other firms to allow SEI to more-than-recoup its investment as TIFIN's enterprise value grows (while still benefitting from being in the early/first-adopter position for its own advisors). While if they're unsuccessful, SEI's investment capital is at risk, but this can still be less expensive for SEI than trying to internally staff and resource an "AI Research & Development" initiative for itself. And TIFIN simply gets both more financial capital to work with, along with a distribution partner to help them pilot new capabilities and then initially bring them to market.

From the broader industry perspective, though, the question is simply whether advisors will really latch onto AI solutions, which ones will gain traction… and whether TIFIN will be the one to build them successfully. As is, TIFIN has been relatively high profile for its eye-popping capital raises in recent years, raising a $109M Series D round in early 2022 at a stunning $842M valuation… only to engage in staff layoffs shortly thereafter, restructure a number of its offerings, and more recently face senior leadership turnover. In fact, relative to its large funding rounds in the past, taking on 'just' $10M for a sleeve of innovation is unusual relative to TIFIN's prior firm-wide raises, with implication that some parts of the business may be gaining traction while others are struggling (such that funding arrangements are now compartmentalizing into select areas). Which means that even if AI is the future, there's no assuredness that TIFIN is the one that will get it built successfully with SEI's investment.

Nonetheless, the SEI/TIFIN deal does represent a classic example of how a large firm can try to navigate the innovator's dilemma, especially in an industry where even the Next Big Thing rarely shows up quickly. And could be a model for how other large firms invest into for the future… and potentially provide a pathway for other early- and mid-stage AdvisorTech funding. Which, like any startup investing, doesn't necessarily clarify who the winners are going to be. But if there's more funding for AdvisorTech in the aggregate, the overall pace of innovation tends to advance in positive ways for all!

Nebo Wealth Partners With Advyzon To TAMPify Its Needs-Based Investing Software Approach?

Goals-based financial planning has been around for more than 20 years, since MoneyGuide first popularized the approach as an alternative to the more time-intensive cash-flow-based planning software of the era. At its core, the distinction was that cash-flow-based tools would look at (and therefore need inputs for) every cash flow in and out of the household (to determine what was saved and what was spent, each and every year of the financial planning projection), while goals-based planning software 'just' needed to capture what was being saved towards that particular goal, and when the outflows for the goal would come due. Which made goals-based planning much faster and easier to implement, driving significant growth for the software companies that facilitated it.

Notably, though, goals-based financial planning was primarily about projecting how much a client needed to save in order to achieve their desired goal, and not necessarily how to invest those dollars. The investment solution was still driven by the advisor's own investment approach to growth portfolio assets to achieve long-term goals – especially given that most goals-based financial planning really entailed just 1-2 major goals that housed nearly all the savings for a multi-decade time horizon (e.g., "save for retirement" and "save for the kids' college expenses").

In more recent years, goals-based financial planning has begun to shift to a "goals-based investing" framework, which attempts to connect the goal more directly with how the assets themselves are invested. Which Morningstar research has found helps to create buy-in from clients (i.e., greater motivation to actually save the way they planned to, and better ability to stick with their investments during volatile times), and also opens the door to even-more-client-customized portfolios where the portfolio doesn't just tie to the planning goal, it ties to the specific cash flows that underlie the goal (and the exact years that they're due to be spent).

This approach of investing a client's portfolio to the exact year-by-year cash flow needs (e.g., for each year's retirement spending goals) isn't exactly new, though. It has long been the framework by which pension funds invest their portfolios – an approach known as Liability-Driven Investing (LDI) – given that thanks to the law of large numbers, pensions can predict with a high degree of accuracy exactly how much will need to be paid out to still-living pensioners in each future year (which constitute future "labilities" of the pension fund). And in recent years, software providers like Nebo (short for "Needs-Based Optimization" of future planning goals) has developed tools to help advisors take a similar approach to building clients' individual retirement portfolios.

The caveat, though, is that the LDI approach for individual clients is much more difficult in practice than it is for pension funds. In part, this is simply because the future liabilities themselves are less certain – personal retirement spending needs in the distant future have more uncertainty than an actuarially-determined pension payout, and while the law of large numbers predicts mortality of all pensioners in the aggregate to estimate how many years payments must continue it's far less feasible to figure out the time horizon of withdrawals for any particular client who might live to 80, or 100, or 115 years old.

More substantively, though, it's also difficult for most advisors to actually implement an LDI-based portfolio for each individual client. As if each client has their own future cash flow needs – based on their specific year-by-year retirement spending goals and time horizon – then every client needs their own combination of cash- and time-specific investments… which can be very difficult to implement at scale across an entire client base, even if software like Nebo can illustrate what the hypothetical portfolio allocation should be.

And so it's not entirely surprising that this month, Nebo Wealth announced a partnership with Advyzon's Investment Management (AIM) platform to provide a pre-packaged trading implementation of Nebo's LDI-style investment approach across an advisor's entire client base (effectively making AIM the back-end TAMP to adopt Nebo's investment approach with clients at scale).

From the advisor perspective, the Nebo-Advyzon partnership makes a lot of sense. It is difficult for most advisors to actually implement an approach where every client's portfolio is customized specifically for their needs – even with automated rebalancing software to manage models, the scalability is challenged when clients don't fit the models because every client's portfolio is different. With the partnership, advisors that like the Nebo investment approach can plug directly into AIM to implement it, and in point of fact Nebo highlights in its announcement that "early adopters have been asking for an end-to-end solution to better streamline and automate the entire process, especially trading and rebalancing"… so Nebo answered their call.

From the broader industry perspective, though, the Nebo-AIM partnership effectively shifts Nebo from being a "software" solution into something that looks more like a TAMP/SMA provider with its own proprietary investment process and its own proprietary front-end software to illustrate that process to clients. Which may be challenging to adopt for the wide swath of advisory firms that already have (and have sold their clients on) their own investment approach, and may not be ready to switch to Nebo's. Yet at the same time, it's also challenging for firms that are less investment-oriented and more financial-planning-oriented… given that traditional planning software doesn't illustrate retirement projections with a Nebo LDI-style approach, and it can be challenging for advisors to explain the 'gap' if they use 2 different financial planning tools (Nebo plus traditional planning software) and have discrepancies between the two.

Which means in the end, the long-term opportunity for the Nebo-AIM partnership will be a question of how big the market really is for the 'Goldilocks' advisor who is not investment-centric enough to be wedded to their current investment approach, and is not planning-centric enough that they're willing to adapt their planning conversations to Nebo's approach. Clearly, there is at least some demand from advisors to adopt more LDI-style approaches with their retiring clients, given the success of similar platforms like Asset Dedication and its $2.1B of TAMP AUM. Though perhaps the real question is simply whether Nebo's design approach – which will need fixed income for the known cash flows in early retirement and equities for the uncertain time horizon of the later retirement years – really ends up producing portfolios with a substantive allocation than the traditional "balanced portfolio" asset allocation that most advisors already use (and can already implement with the tools they have)?

MSCI Acquires Fabric Risk As AdvisorTech Software Adoption To Build "Personalized Portfolios" Remains Sluggish?

The whole point of providing advice – as contrasted with generic rules-of-thumb – is to craft recommendations that are in the client's best interests, and are specific to the client's needs and circumstances. Historically, this was a challenge for an industry that was compromised mostly of product manufacturers and distributors, who hired salespeople with the end goal of selling (the same) company product to anyone and everything they met. But over the past 20 years, as the financial advisor world has increasingly shifted to actual advice over product sales, the advice recommendations have become increasingly personalized.

The caveat, though, is that when it comes to not just personalized advice but implementing it into a personalized portfolio, the advisor needs to both gather information on clients' goals and preferences, and then needs some method or measuring stick to determine which investments will do the best job of actually fitting those client-specific parameters. For instance, if clients want environmentally friendly stocks to personalize their investments to their preferences, the advisor has to figure out how to measure "environmentally friendly" and where to draw the line on what's in or out of the portfolio. Similarly, if clients want a lower-risk portfolio based on their specific goals, the advisor has to determine how to measure that risk to find the right investments. Is risk volatility? Or ability to achieve the client's goals? Or is it sensitivity to certain market risks? Or something else?

In that vein, Fabric Risk was founded in 2019 with the stated goal of "bringing the risk analyses used by large institutions" to individual advisors when analyzing client portfolios. At its core, Fabric brought various analytical tools to evaluate a client's exposure to various market segment, factors, and other unique risks (e.g., climate risk), built around market data from MSCI, and then forward-project how the client's portfolio might respond to various future market scenarios (given its risk exposures). Which then created the potential for advisors to differentiate themselves in the nature of the portfolios they built for clients (using Fabric's unique risk analytics), and create more customized client portfolios (based on how they want to weigh these various risks and other exposures relative to the client's goals and preferences).

But now, the news is out that Fabric Risk has been acquired by its data-provider MSCI, and is effectively being brought in-house to support the sale of MSCI's own rules-based portfolio offerings for financial advisors serving individual clients.

From the advisor perspective, the good news is that the Fabric acquisition will not be disruptive for most. Kitces AdvisorTech Research showed very little material adoption of Fabric Risk in the first place (at least amongst independent advisors), which means few will be affected by the acquisition. And in practice, to the extent that MSCI is bringing the software in-house to support advisors implementing portfolios leveraging MSCI capabilities, advisors using Fabric and building portfolios around MSCI's approach will likely be just fine with working even more closely with MSCI anyway. And arguably, given MSCI's sheer reach with nearly $2 billion of revenue, Fabric may see even more adoption in the hands of MSCI as its own proprietary sales/illustration tool to express its own data in meaningful ways to advisors.

From the industry perspective, though, the Fabric Risk acquisition highlights that, notwithstanding all the industry talk about building more personalized portfolios for clients, advisors seem to be slow to adopt actual third-party technology tools to actually implement such approaches. In part this may simply be a result of the fact that each tech tool offering to facilitate customization has something proprietary about the metric it uses to measure and personalize… which means advisors can't just adopt the technology, per se, they often have to change their entire investment approach and conversation with clients to fit the software's approach. Which effectively means the transaction is no longer a software purchase, it's a change to the advisor's entire investment philosophy (which at best is something advisors only change very rarely). And to explain why such tools are being folded into investment managers that can actually implement the end-to-end portfolio management approach (not just as software); after all, if the advisor is going all-in on the software's modeling approach, they're probably going all-in.

In the long run, though, if advisors are increasingly financial-planning centric, and that is how advisors get more and more specific in their recommendations to clients' needs and goals, and making clients feel heard and understood… the question remains about whether advisors will really migrate towards more personalized portfolios (and the scalability challenges they present), or will advisors continue the recent trend towards more passive ETF-style portfolios instead, and hang their hat on the value proposition of personalized advice over personalized portfolios (which makes the technology for more portfolio customization a moot point)?

AI Startups Suddenly Abound To (Pre-)Qualify Prospects In The Historically Analog World Of Advisor Prospecting

In the traditional world of growth, marketing makes the phone ring, and sales is what you do when you answer the call. But in the financial advisor world, the idea that the phone would ring because a prospective client is calling is often the punchline to a joke – roughly akin to the idea that if you wait long enough, fish will voluntarily jump into your boat. Instead, the reality is that for most advisors, if you want to get new clients, you have to go out and "prospect" (search) for them.

One of the biggest challenges with in-person prospecting, though, is that it's difficult to know who would actually be a good prospect, until after you've already spent some time with them to learn more about their situation. While some tactics may provide some increase to the general odds – for instance, networking at the country club with a high entry minimum probably increases your odds of mingling with people that have more financial wherewithal – one of the first steps in most prospecting processes is to try to determine if the prospect is "qualified" (i.e., would be interested in your services, and has the financial means to pay what your services are worth). Which means taking the time for a conversation with each and every prospect you meet to find out.

In recent years, the rise of social media and the internet has made it possible for a growing number of advisory firms to create more "inbound" marketing funnels, where the firm doesn't have to go out to find prospects, and instead creates content (e.g., blog articles, podcasts, videos, etc.) that educate consumers and demonstrates their expertise, and creates a list of contacts who may eventually become prospects. Yet still, the blocking point is typically that the advisor must actually conduct an initial 'get-to-know-you' meeting for 15-30 minutes to figure out whether the prospect is really a qualified prospect. Which, again, can be time-consuming to separate the wheat from the chaff.

Enter AI. Because the reality is that in an increasingly digital world, most 'strangers' have a non-trivial amount of information available about them on the internet (not to mention more information through the marketing industry's consumer databases), and more affluent individuals often have even more information available (e.g., because they're licensed in their professional industry, are in the news in their chosen profession, are publicly listed as a key employee in their business, have required disclosures as an executive trading in their company stock, etc.). Which means if financial advisors can tap into this information, "qualifying" a prospect could be as simple as turning over the prospects basic contact information (e.g., name and email address, and perhaps home address or telephone number) to a piece of software that scrubs available online information to determine how qualified they really are.

Which is exactly what a number of recent startups have launched for financial advisors, leading to the addition of an entire new "Prospecting" category to the Kitces AdvisorTech Map. For instance, Catchlight scans a wide range of marketing and other public data sources to put together a profile for every name and email address it's provided, and then ranks a list of leads based on how likely they are to fit the advisor's specified qualifying factors (e.g., age, income and net worth, business ownership, etc.), and even helps to pre-write an initial outreach message for the advisor to send. AIdentified similarly scrubs various data sources, based on a list of contacts that can be imported (e.g., via Google, Outlook, or LinkedIn connections), and then provides real-time notifications of significant events (e.g., the client's company IPOs, or they announce a job change) that might be an opportunity. FINNY provides a similar scrubbing of contacts for qualified prospects, but can also take input of advisors' prior prospects who turned into clients, leveraging its technology to try to identify additional traits that might further help prioritize the most valuable qualified prospects. Wealthawk offers to analyze not only prospects to pre-qualify them, but can also scan existing clients to identify their additional money-in-motion events as well. While Equilar tries to help advisors reach the highest-value prospects – those with whom the advisor may not even have a connection yet, but Equilar scans available information to figure out who in your existing client base and network might have a connection to those prospects, so the advisor can pursue an introduction.

Ultimately, the biggest caveat to all of these tools is that technology to better (pre-)qualify a prospect is only helpful for advisory firms that have a healthy flow of prospects to qualify, by whatever analogy or digital means they build their flow of leads. And in practice, for a lot of advisory firms, getting any prospects into the pipeline – qualified or not – is their biggest problem, not the challenge of "too many prospects, too little time to figure out which ones are good prospects or not". Nonetheless, even for advisors with a relatively modest flow of prospects, anything that helps to focus time – if only to help make it clear which prospects the advisor shouldn't realistically spend any time with, and which might be better opportunities than realized and worth leaning in to – is arguably valuable. But realistically, the market opportunity for prospecting tools is likely to be limited to a relatively modest subset of advisors who are generating enough growth opportunities that it's worth going through the cost of purchase and the time to setup and configure software to help them winnow down the list.

Which means in the end, it may be difficult for half a dozen or more different AI (or at least, machine-learning or rules-based algorithm) prospecting tools for advisors to survive and thrive. (And in reality, most AdvisorTech categories, from emerging tech to stalwart incumbents, have no more than 3 major players with material market share.) Nonetheless, qualifying prospects, and knowing which ones are likely a bad fit, as well as those that may be an especially good fit, has long been a point of frictional inefficiency for financial advisors. And so while the marketplace will sort out which tools will be most likely to survive (with a likely tilt towards the tools that can help advisors maximize a small list of prospects, over those that try to winnow does a large list), the new category of prospecting tools seems likely to stay.

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In the meantime, we've rolled out a beta version of our new AdvisorTech Directory, along with making updates to the latest version of our Financial AdvisorTech Solutions Map (produced in collaboration with Craig Iskowitz of Ezra Group)!

So what do you think? Is it more appealing to consider Salesforce FSC with more multi-custodial integrations available out of the box? Have you paid for leads from a lead generation platform (or thinking about doing so)? Would it be helpful to have a piece of technology that quickly analyzes a new prospect to highlight whether or how much time you should spend pursuing the opportunity? Let us know your thoughts by sharing in the comments below!