Look at any recent ‘Trends in Wealth Management’ white paper, and you’ll see personalization feature heavily. (We’re guilty too—our latest is Purposefully Personal: Crafting a High-Growth ESG Client Experience.

It’s got my wheels turning about what a winning personalization strategy looks like for wealth managers. Based on many conversations with wealth management leaders, it feels like most of them are unwittingly heading into a ditch of undifferentiation. Tracing it back to its root cause, the acid test question is: are you relying on “found” data or are you “creating” unique client data to underpin your personalization strategy?

CapGemini’s 2020 World Wealth Report helps illustrate the point. CapGemini took a look at the client experience (CX) for high-net-worth individuals (HNWIs) around the globe. The money graphic from the report is this one:

Sample size 2,500 + from 21 countries around the world.

Top client pain points are:

  • Researching info about your firm
  • Receiving personalized updates about new products/services
  • Receiving educational market information
  • Receiving value-added services

CapGemini goes on to call out lack of personalized information and services as the Achilles’ heel for wealth management. I think that’s generally a fair observation, but I’d put some finer points on it.

Looking at the pain points, I’ll address the first one in a future blog post.* Let’s focus on the other three touchpoints—receiving updates about new products/services and value-added services, and receiving educational market information.

These could certainly all benefit from greater personalization. After all, clients’ expectations of these kinds of touchpoints are certainly rising, thanks to Amazon’s and Facebook’s personalized experiences that trigger this thought bubble: That’s amazing-slash-creepy! How do they know me so well?

Wealth management firms aren’t taking this lying down, of course. They’re investing in data platforms, AI, machine learning and all the elements that will help them personalize these touchpoints.

But here’s the thing—they’re likely to end up in a personalization arms race where they all have access to similar kinds of “found” source data, and that’s likely to lead to undifferentiated personalization.

Diving into this point a bit deeper, the largest wealth managers all have their first party data on clients. They’ve got demographic data with details about the client’s age, family size, education and so on. They’ve got data about their financial situation—assets and liabilities; cash flow; and the like. And, they have transaction data—financial transactions (deposits, withdrawals, trades, etc.) and communications transactions (website behavior; email response rate and click behavior; etc.). These data can all be useful for personalization, but it isn’t all that differentiating. Every wealth management firm has this kind of data about their clients.

Many firms are enriching their first party data with third party data available via data brokers—what kind of car a client drives, propensity to have certain health conditions, whether they are a pet owner, a fitness enthusiast or a flower enthusiast, travel patterns, and so on. But these data are, in many ways, highly commoditized, too. All of the major data brokers, from Acxiom to Experian, provide this kind of data. Do they help understand a client more fully? Sure. Do they serve as the basis of a different form of personalization? Not really.

As a result, most wealth management firms will show up to the personalization party wearing a very similar looking outfit.

The personalization leaders, however, will show up wearing something very different. They will have focused on how they can create their own, unique first party data to bring to the mix—data (and insights) that are very difficult for competitors to replicate. The operative words here are “create” and “unique”.

What does that look like? Well, among the places to look for this kind of unique data are behavioral economics and decision science. There’s an emerging set of client profiling methods grounded in these disciplines that generate unique data and insight into what makes clients tick. Their loss aversion (separate and distinct from their risk tolerance); their level of conviction to various environmental and social causes; their level of economic rationality in their decision making; and so on. To learn more, explore the Capital Preferences Science page.

Decision scientists call this kind of data “experimental”, because it comes from running structured, interactive “experiments” (i.e., think of them as simple, quick decision games) with clients that reveal their “why”s. That’s why this kind of data isn’t simply observed—it’s actually created.

These are data points that no data broker can provide. Moreover, wealth management firms would be hard-pressed to “intuit” these kinds of insights from their own first party data. That’s because they go beyond simple “whats” about clients (what behaviors they engage in), and delve directly into the “whys” behind the “whats” (why they make decisions and engage in the behaviors they do). I’d argue the “whys” are a more powerful basis for personalization in wealth management, and they certainly represent the quickest path to differentiated personalization.

Don’t settle for speed bumps when you could build a competitive moat.

* The first pain point is “Researching info about your firm”. Why is this such a pain? It’s certainly not for lack of readily available information about wealth management providers. On the contrary, it’s a function of “information and choice overload”. Plenty of studies detail how too much information can be confusing and overwhelming to customers and can lead them to analysis paralysis. When they go do their research on wealth management firms, they drown in a sea of information that all starts to feel the same, and struggle for a basis on which to differentiate. I’ll do a future blog post on this one—it requires a unique approach to personalization.