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The Struggle To Optimize Fee-Only Models Is Real

Umm, you wanna get in on this logit regression?

Financial advisors face uncertain times ahead with the implementation of the DOL fiduciary rule. In light of that challenge and other changes roiling the industry, many advisors are moving a fee only model. The question they face is – what fees should they charge.


Charge too much and you will have high margins but at the expense of client retention. Charge too little and clients will stick around, but be unprofitable. An alternative to this is to charge different clients different amounts based on their willingness to pay for advice. Data analytics can be a great tool for the financial advisors looking to do this.

There are two problems standing in the way though. According to a survey done by the Wall Street Journal, the biggest single problem that businesses face in effectively using data is that they don’t have they data they need. The second biggest problem, in my experience as a consultant, is that FA firms do not understand what data analytics can do.

Pricing analytics is a huge untapped opportunity that firms across the economy are only just starting to take advantage of. Pricing differentiation is taking hold in travel for instance – look at a price for a plane ticket on your smartphone versus a computer and you will probably get two different prices. Look for a plane ticket price on a weekday versus a weekend, and you will also get two different prices. These prices are driven by data analytics. Financial advisory firms can take advantage of the same concept.

The basic idea here is simple – if you charge all clients $20o a year, you will have clients calling all day, but you won’t make any money. If you charge $500,000 a year, you would be enormously profitable if you could keep your existing clients, but they will all leave.

The key then is charging clients their maximum willingness to pay. Some clients are willing to pay a lot because the attorney adds tremendous value to the client’s business, and other clients are much more willing to shop around.

Pricing analytics can also be extremely helpful in winning new deals and clients. Financial advisory firms dealing with big institutional clients know that they are often not the only firm being considered for the work. And of course, price is not the only factor by which clients make a decision. But it is a factor. Data analytics can help FAs put together a client proposal which maximizes the chance of getting a new client, and making that client a profitable one.

The quest for maximizing profits by charging differentiated prices starts with what is called a two-stage regression. Advisors need to make sure that the price they charge will win the confidence of a new client, and will not drive an existing client away. This requires using a logit regression.

A logit regression examines multiple different variables related to the client and the FA, and then determines that probability of those clients leaving (or not engaging the FA in the case of prospective clients) based on a mathematical formula. The data needed here is wide-ranging: base fees of course, as well as other factors like FA experience, number of competing FAs for the same business, geographic region, etc.

Where does the data for this logit regression come from? It has to be gathered. That process is not easy, and it requires an investment of firm resources, but it can make a big difference in firm profitability and client retention. (A similar analysis can also be done to help the firm proactively identify the clients at greatest risk of leaving the firm, so that special efforts can be made to keep them on-board.)

With the logit regression complete, it’s time to consider the second stage of the two-stage regression analysis. The second stage of analysis requires taking the output from the mathematical equation in the first step, and then using it to modify the fee charged for each client so that it maximizes overall firm profitability.

Some of this may seem complex, and undoubtedly many FAs would prefer to just continue using their tried and true business model. Doing that is leaving money on the table though – numerous studies have found pricing differentiation is more profitable than flat pricing. Data analytics has a learning curve certainly and requires an upfront investment of money and time by firm partners. But once that investment is made, the results can pay major dividends for the firm over time. In fact, studies on pricing differentiation in the broader business world have found that returns on investment for data analytics average 56% annually. That’s a figure which should be welcome news to any firm looking to use their resources effectively.

Mike McDonald is a PhD in finance and a university professor in the subject at Fairfield University in Connecticut. He also runs a consulting company doing work on quantitative investing, big data, and machine learning for a variety of financial firms, asset managers, institutional investors, and government regulators. Prior to getting his PhD, Mike worked for a major Wall Street bank and one of the top hedge funds. Comments, questions, and concerns are always welcome – email Mike at or visit his firm’s website at



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