The numerous personalization opportunities out there should mean that investors are more empowered than ever to create customized financial plans that fit their preferences and financial goals. Right?
Although they’re technically available, these opportunities for personalization may not always be accessible to the average investor—partly because of how the industry approaches portfolio personalization.
For instance, some advisors may expect that investors are going to walk into their office and ask for things like environmental, social, and governance-focused investment options or direct indexing capabilities. And although some might do so, most investors either have no idea such things exist or only have a vague notion of them.
In our research, we find that most investors’ top priority is to reach their financial goals—more so than optimizing for tax efficiencies or ESG preferences. However, this does not mean that personalization options (for example, ESG, direct indexing) aren’t important. But it does mean that advisors must help clients become aware, recognize, and consider these other opportunities.
To help introduce clients to portfolio personalization options without overwhelming them, advisors can take a few lessons from data-driven recommendation systems.
Foldable Yoga Mats Exist?
Let’s consider Amazon.com’s recommendation system.
Instead of leaving it to the consumer to find a product that fits their needs, Amazon focuses on identifying a person’s potential needs and connecting those needs to available products. This tactic allows Amazon to suggest products that the consumer may have never considered or may not even know about. Amazon may know what consumers want before they even do.
For example, the other day, I saw an ad on my Amazon homepage for a foldable yoga mat. I never told Amazon I wanted this—or even knew such a thing existed—but I realized it was a perfect solution for staying active while traveling. Amazon had guessed this product would fit my needs by learning from my behaviors and preferences. And now, it has a happy customer who just spent more money on its platform.
Amazon (and other personalization giants like Netflix, Spotify, and so on) are able to make these connections using powerful recommendation systems. These systems are fueled by a hybrid of approaches like collaborative filtering, content-based filtering, and machine learning.
This doesn’t mean you need to run out and brush up on your coding skills. But advisors can take a few notes from the output categories of these recommendation systems.
4 Categories of Recommendations to Consider for Clients
The recommendation systems of companies like Netflix and Amazon are successful because they use different strategies to predict a person’s next possible purchase or song choice. Then, these strategies appear as categories to us mere mortals.
Here are a few that might make introducing personalization options to investors a bit easier.
Recommended for you: On Amazon, this category is driven by a person’s recent browsing history or recent purchase—for example, a person might see more hiking products if they just bought a hiking pole.
Consider a client who just asked about the implications of receiving a large amount of their employer’s company stock. Soon, he may also be interested in the tax implications of selling that stock.
It’s worth thinking about how services naturally bundle together and being ready with, “Now that you’ve considered X, have you thought about Y?” By staying one step ahead of your clients, you can better help them get where they want to go.
Frequently bought together: Amazon’s algorithms can provide suggestions based on the product you just bought and what other people usually buy with that product. For example, other people may buy dog bowls along with puppy food.
In financial planning, take someone who just decided to invest in an asset that aims at advancing the use of renewable energy. They may also be interested in an asset that avoids fossil fuels.
Bestselling in category: At times, Amazon or Netflix will present the top items in a category, even if the person has yet to engage with that category. In some ways, this can be a soft introduction to a new category that avoids the trap of choice overload. Instead of presenting users with all the options in a category, which might scare them from engaging with it at all, the systems narrow the options to the top-rated.
In financial planning, this can be as simple as providing an overview of the various personalization services you offer but only presenting the “top-selling” options in each category. The truth is, many clients don’t know what options are out there, and providing this narrowed overview can open their eyes without overwhelming them. It also uses social proof as a way to help promote engagement.
Trending products: This category can be based on seasonal trends (holiday-themed films) or one-off blips that affect consumer behavior (pandemic-themed documentaries).
In financial planning, there are certain times of the year that people may be more receptive to certain products. For example, implementing a tax-loss harvesting strategy may be more attractive around April, or 529 plans may be more top-of-mind in September.
Keeping Clients’ Best Interests in Mind
In practice, these concepts can be seen as a way to keep the discovery phase going throughout your relationship with a client.
When first meeting a client, you’ll probably still want to follow the traditional process of addressing their immediate concerns. But as you develop the relationship, it’s important to help the client “dig deeper” through conversations, exercises, and more. It’s during these deeper conversations that the client (and you in the process) discovers their own needs, goals, preferences, and values. Based on the output of these conversations, these personalization categories may start to emerge.
One key detail to keep in mind as you consider putting these recommendation system categories into practice: Financial advisors think as fiduciaries, marketplaces do not.
Advisors should view this “recommendation systems” approach as a way to help connect clients to things they need and to solutions that exist—even ones they have not yet considered. It’s not about selling unneeded products to unsuspecting buyers but about helping a client connect today’s choices to long-term financial goals.
The author or authors do not own shares in any securities mentioned in this article. Find out about Morningstar’s editorial policies.