With the ongoing rise of advisor FinTech, one of the “hot” new categories starting to emerge are “Advisor Matching” services – websites where consumers can enter some personal information, and get suggested “matches” of potential advisors who can help them with their financial issues. Ideally, consumers get a potential advisor who can solve their problems, and advisors save time by being matched to prospective clients who are a good fit for their services.
However, thus far the ideal rarely matches the reality. The fundamental problem with building a “Match.com for Advisors” service is that it’s almost impossible to effectively screen and filter a list of potential advisors when most advisors are generalists who work with a far-too-wide swatch of consumers in the first place. If advisors don’t differentiate themselves, an advisor matching service can’t differentiate amongst them, either! And ironically, the advisors who do differentiate themselves with a niche or specialization will likely have better sources of clients than a generalist advisor matching service anyway!
Compounding the challenge of creating a viable advisor matching service is the simple reality that it’s expensive to market and build the trusted brand – whether as an advisor, or a “trusted” website to find one – necessary to generate a sufficient volume of highly qualified prospective clients. Which means in the end, even though advisors will pay astonishingly well for new clients – which means a “Match.com for Advisors” solution will be highly lucrative, if the puzzle can be solved – the low trust in financial services and incredibly high costs of client acquisition may be just as destructive for advisor matching services as they are for the advisors being helped.
Advisor Review Sites, Registries, And Matching Services – The Need For Lead Generation
Getting clients has always been hard for most advisors, who increasingly are entering the financial planning profession out of a desire to help people, not necessarily as entrepreneurial business developers and salespeople. And with organic growth rates slowing at many advisors firms, there seems to be an increasing interest from advisors to get some marketing help, or simply “outsource” their prospecting efforts altogether by paying for lead generation services.
Accordingly, in the past few years there has been an explosion in new services offering to funnel potential new clients to advisors. The concept is relatively straightforward – the website will gather a high volume of prospective clients, and then help those clients find a “good” advisor for them. From the advisor’s perspective – the advisor doesn’t have to go find clients, because the website brings the prospective client “leads” to them instead.
To create the leads, some sites try to build around providing advisor reviews so consumers can choose accordingly, a “Yelp-for-advisors” model that unfortunately is hopelessly flawed due to the small number of clients that most advisors have to give reviews, along with the SEC’s anti-testimonial rules. Alternatively, the site might create a select list of “quality” advisors that are screened and vetted for consumers to choose from (an “advisor registry” of sorts). With these models – like Paladin Registry, or the NAPFA or CFP Board Find-An-Advisor services – the site lists all the [vetted/screened] available advisors, and the consumer searches for a match.
The alternative approach that some companies are now taking is to become a “matching” service, by asking questions and gathering information about the consumer and trying to match the right advisor to the prospective client’s individual needs and circumstances. In this approach, the client doesn’t just search through reviews of advisors or a standard pre-vetted listing of them to select one; instead, potential advisors (who may also have reviews and/or vetting) are delivered based on an algorithm that aims to match the consumer and advisor based on the details of each.
Recent entrants to this “Match.com-for-Advisors” model include services like GuideVine, the new EF Hutton Gateway, and the AdviceMatch solution from BrightScope. And judging from the rising volume of inquiries for my own Consulting services, there may be several more launches of similar services later this year as well.
Given the desire for growth, the fact that most advisors generate a relatively high revenue per client (which allows for significant compensation to lead generation and referral sources, when they work), and the otherwise challenging costs of client acquisition for advisors – not to mention that consumers themselves also seem to struggle with the process of differentiating between advisors to find a “good” one – the business case seems quite compelling.
Except for one fundamental problem: there’s no good way to filter and screen advisors to match them with consumers in the first place.
A Match.com or eHarmony Algorithm Couldn’t Differentiate Between Undifferentiated Generalists Either!
The effectiveness of couples-matching sites like Match.com and eHarmony lies in their ability to screen and filter candidates on a wide range of different factors (eHarmony focuses on the fact that it uses 29 dimensions to establish a match!). With so many factors, it becomes feasible to determine both who would not be a good match, and who likely would be, with lots of criteria to line up. In fact, the more criteria there are available, the more efficient the algorithm can potentially be.
When it comes to financial advisors, though, the problem that quickly arises is that there are remarkably few factors to use for filtering and screening in the first place. Some sites start by filtering based on assets or net worth – given that many advisors do have investment or fee minimums – yet the fact that a prospective client is wealthy enough to pay the advisor doesn’t necessarily mean the advisor is a good fit for the client. (I.e., just because an advisor is “willing” to work with millionaires, doesn’t mean the advisor is actually any good at working with millionaires!)
In turn, some sites aim to get a step deeper, and measure a potential match based on factors like life stage or recent life events of the client. Yet here, again, the reality is that those more commonly define the need for an advisor (which paired with assets/net worth demonstrated an ability to pay for an advisor), but still doesn’t clarify which advisor would really be best to work with.
For instance, almost any advisor would be “happy” to work with a recent widow who received a $2M life insurance death benefit in cash and needs help investing it – the death of her husband may have triggered the need for an advisor, and $2M may mean she has the wherewithal to pay well for an advisor’s services, but if she had $2M because she just retired, or due to a recent divorce, or as a result of the sale of a business, most advisors would probably be equally happy to work with her. The fact that an advisor will work with such a prospective client doesn’t actually mean the advisor has any kind of specialty with recent widows (or divorcees, or new retirees, or those who are selling a business); in fact, if you ask which “types” of clients an advisor is willing to work with, most would simply check every box that could potentially come to the table with $2M!
Similarly, some sites present questions about what areas the prospective client is looking for help with – do they want investment management, or retirement advice, or an insurance review, or help with their estate plan, or guidance about the best college savings strategies, etc. Yet here, too, the problem is that a comprehensive financial planner is at least moderately trained in all these areas. The end result – the client might select just one or two check boxes of “need”, but the generalist financial planner says he/she can work with clients with any of those needs and checks all the boxes for being a potential “match”. Which means for the prospective client, “every” advisor is a match, and there’s still no effective screening or filtering process!
In fact, there is actually so little basis to filter through and differentiate amongst advisors, that for many matching services, the primary or sole effective criterion is simply the location of the advisor. In other words, when all else fails as a means to screen a list of prospective advisors, the zip code is the one remaining factor that really can filter down the potential matches, as there are only so many advisors in any one particular geographic region.
With the caveat, of course, that the geography of the advisor says very little about their actual skills, experience, expertise, quality, or anything else that is actually relevance to the success of an advisor-client relationship! Given today’s digital tools for online meetings and collaboration, it’s increasingly feasible to work with the best expert – period – not the best expert in your local neighborhood. In today’s environment, using geography to pick an advisor is the dating equivalent of filtering a search for “a [male/female] within 30 miles of the 21046 zip code” and expecting to be matched to a person you’ll want to someday marry – doubtful, to say the least! There are better ways to find a soulmate – or a good advisor – than the distance you’ll have to drive to meet him/her for the first time.
Is The Matching Service Model Fundamentally Broken For Advisors?
Ultimately, the reason it’s so important – and concerning – that matchmaker services cannot effectively differentiate amongst undifferentiated advisors, is that when the primary criterion is simply geography, the service isn’t really adding much value beyond what a mere Google search can provide. After all, consumers can determine their own narrowed-down list of advisors based on geography, simply by doing a Google search for “financial advisor [cityname]” – or simply searching for “financial advisors” with location services turned on, and let Google do the location screening automatically on your behalf!
In other words, matching services – whether it’s Match.com or eHarmony, or an advisor matching solution – will live or die in the end by their ability to actually create successful matches, above and beyond what the consumer could have done on their own with freely available search tools. If the search/match results aren’t materially “better”, consumers won’t adopt the service (and tell others), and it will be increasingly more difficult and more expensive to bring in visitors to use the site in the first place.
In fact, the problem that most advisor matching services seem to eventually succumb to is the sheer cost of their own client acquisition – the web traffic that comes to the service to search for an advisor, which is ultimately intended to be delivered to the advisor as a (paid) lead. If the advisors aren’t very differentiated, the conversion rate (site visitors who eventually go all the way through the pipeline, find a good-match advisor, and sign on to work with him/her) will likely be very low, and if the conversion rate is too low then the site simply won’t be able to charge advisors enough to recover the cost of getting traffic in the first place.
For instance, if a site has to pay $5 per click to Google Adwords to get a visitor, and only 10% of the visitors actually go all the way through the matching process (because not everyone wants to do the “work” of answering questions), and the advisors only manage to “close” 25% of the “perfectly matched” consumers that are referred to them (because geography isn’t actually a great way to make matches), then the site is only going to close a referral for one out of every 40 visitors, which means it has to charge $200 per closed lead just to break even on the cost of client acquisition!
In turn, if the site has 1,000 advisors and wants to deliver an average of one client to each advisor every month, it has to draw in 40,000 visitors, at $5 each, or spend $200,000/month on advertising alone. And arguably, a 10% conversion rate is quite high, as is a 25% close rate for geography-based leads (with little other effective screening criterion). If these numbers are “just” a 4% conversion rate and a 10% close rate, the site now has to draw in 250,000 visitors per month, at an advertising cost of $1.25M per month with a $5/visitor acquisition rate. And of course, this doesn’t count any/all other expenses of launching the service, building the website, and staffing and operating the business; this is just the cost of client acquisition to produce the leads!
Of course, the upshot here is that marketing is something that can scale effectively with time and resources, so a large advisor matching service might be able to bring down the cost of client acquisition over time, improving upon the business model (for instance, BrightScope can also leverage consumer traffic through its 401(k) look-up service and its Advisor “due diligence” pages). Indirectly, this is why lead generation services like the advisor networks from the major RIA custodians work so well; those companies are able to leverage their existing brand, infrastructure, and client acquisition models (in addition to the fact that their platforms and referral agreements allow them a far better means of monetizing the leads, in some cases as much as 25% of revenue the client pays to the advisor!).
Nonetheless, even with scaled marketing to bring down the cost of client acquisition, the challenge remains that when the criterion for advisor searching/matching remains weak – if only because the advisors don’t clearly differentiate themselves – it will be difficult to achieve the kinds of conversion rates necessary to sustain the model, especially as the number of advisors on the platform grows and the ability to filter amongst them becomes increasingly important to provide a value-add for the end user.
Will Advisor Niches And Greater Specialization Save Advisor Matchmaking, Or Make It Irrelevant?
Perhaps the greatest irony of the challenge for advisor matchmaker services – that advisors don’t do a very good job of differentiating themselves with a niche or specialization, so it’s hard to screen for appropriate matches – is that the situation isn’t necessarily better when advisors do differentiate themselves.
After all, in an environment where most advisors do have a specialization or niche, the focus of their marketing will likely be in their niche to reach their target clientele, not necessarily on a generalist advisor matching service! Alternatively, to the extent that a consumer is searching for a (specialist) solution, Google once again can be remarkably effective; for instance, you can just type “financial planning for doctors” to find Larson Financial, or “financial advisor for Gen Y” for Gen Y Planning, or “financial planner for teachers” to get Finance For Teachers, etc. In other words, when advisors are generalists, it’s challenging for advisor matching services to be any better than (location-based) Google results, and when advisors are specialists… it’s still challenging for advisor matching services to be any better than (keyword-based) Google search. And the advisor matching service still has the cost-of-client-acquisition problem.
Accordingly, it’s probably no coincidence that some of the most successful advisor “lead generation” platforms to date have been the ones where the nature of the platform itself is a form of niche. For instance, NAPFA has built much of the success of its “Find An Advisor” leads for members around being at the forefront of the “fee-only fiduciary” niche (though admittedly not quite the differentiator it once was), and similarly the Garrett Planning Network’s “Find An Advisor” service has built its success around Garrett model’s “hourly financial planning for the middle class” brand. Our XY Planning Network “Find A Gen X or Gen Y Advisor” service is gaining traction following a similar strategy as well. In essence, these services are succeeding because the nature of their platform itself is differentiated from the alternatives (whether fee-only advisors, hourly advisors, or Gen X/Y advisors), and their unique brand and earned media has allowed them to generate web traffic for leads without the otherwise problematic costs of client acquisition.
Notwithstanding all these challenges, though, the fact remains that because client acquisition costs are so high for advisors, and lead generation is such a challenge, solving the lead generation puzzle will be an incredibly lucrative business opportunity for whoever can figure out how to do it successfully. Nonetheless, the catch-22 of it all is that in today’s environment, advisors may simply be too undifferentiated for such a service to be effective, and in the future as advisors increasingly do specialize and find niches, such a solution may not be necessary anyway?
So what do you think? How much would you pay for bona fide leads from an advisor matching service? Do you think an algorithm can successfully match consumers and advisors, with better criteria than “just” geography? What criteria do you think are relevant in trying to match advisors and consumers?
Disclosures: Michael Kitces is a member of the Advisory Board for BrightScope, and is a co-founder of the XY Planning Network.