As baby boomers prepare to transfer up to $70 trillion to their Gen X and millennial children, the number of mass affluents combined will grow significantly and also shift younger.

While mass affluents might desire the white-glove service traditionally provided to HNIs, their assets under management (assuming a standard annual wealth management fee of around 1%) cannot support the person-to-person approach of a traditional wealth management advisor. Serving mass affluents is all about efficiency, and to serve mass affluents at scale, wealth management firms are exploring several different tactics. Many have turned to digital solutions like robo-advisors, automating functions like risk/reward surveys and portfolio rebalancing, with mixed results. Some have expanded their product offerings beyond equities and fixed income, giving retail and mass affluent investors access to investment vehicles like insurance and loan products, SMAs and digital assets (cryptocurrency). To compete with fintechs, some firms are also doubling down on UX, embracing the intuitive visualizations and self-service experiences that their increasingly Gen X/millennial mass affluent customers now expect.

While each of these tactics is making an impact, wealth managers that want to truly win the mass affluent segment must synthesize all of these tactics (and more) into a unified hyper-personalization strategy. For high net-worth individuals, financial advisors now function almost like life coaches, anticipating and helping clients prepare for major life events, from going on vacations and paying college tuition to choosing the right life insurance coverage and considering the taxation implications of cross-country moves. To serve mass affluents, advisors often had to abandon this holistic approach in favor of inflexible automation. New digital capabilities, however, will empower wealth managers with the agility to respond to the individual life journeys of their mass affluent clients through hybrid advisory models, new hyper-personalized services, tracking engines that use data-driven insights to hyper-scale wealth manager expertise, and seamless new onboarding experiences.

Building an Efficient Hybrid Advisory Model with Smart Sub-Segmentation

Mass affluents’ financial needs differ considerably depending on their age, family composition, risk tolerance and previous investment experience. Intelligent sub-segmentation (drawing on data from both intake surveys and AI/ML analysis) is emerging as the optimal way to serve such a diverse client segment. By sorting mass affluents into as many as 20 (or more) distinct sub-segments, wealth managers can offer products and services relevant to each client’s current life trajectory while still gaining the efficiencies of automation. Factors as specific as expensive hobbies (like golf or skiing) and personal values (environmental conservation or philanthropic interests) can inform intelligent sub-segmentation.

Fortunately, sub-segments on the lower end of the mass affluent spectrum will not require a high-touch approach. These investors tend to be digital natives who are comfortable with mobile apps and may have previous experience buying and selling securities (including stocks, but also cryptocurrency and even NFTs) through online platforms.

Sub-segments on the higher end of mass affluent spectrum, particularly those approaching $1 million in assets, may desire a more fully personalized experience and require periodic one-on-one touchpoints. For these clients, traditional wealth management firms have a distinct competitive advantage over newer fully-digital incumbents—if they can also build efficiencies into these personal touchpoints. One-to-one interactions with mass affluents should be structured to deliver rapid insights and point to the digital resources and tools that can enable clients to make autonomous decisions with confidence. These interactions should also be prioritized based on a data-driven analysis of Customer Lifetime Value.

In one recent project with a large bank in the Middle East, we found that a new sub-segmentation strategy improved the bank’s product penetration rate per customer, increased the bank’s overall NPS score, and drove both short- and long-term strategy insights and decisions. In particular, the project allowed the bank to compare the performance of new personalization-driven offerings to its standard non-personalized approach, revealing the advantages of efficient and data-driven personalization.

Engaging Mass Affluents with Hyper-Personalized Services

With a sub-segmentation strategy in place, the true work of providing hyper-personalized services at scale can begin.

Innovative wealth managers are optimizing their customer journeys through gamification, providing clients with specific products and goals that sync with their sub-segment’s current life situation, and issuing rewards when they engage with those products and complete those goals. Nudges and actionable insights can surround the gamified features with sound, data-based recommendations.

On the client side, hyper-personalization needs to feel open-ended rather than overly proscriptive. To that end, goal-based planning and portfolio rebalancing tools in a wealth management platform—like an in-app calculator that helps clients visualize the impact of small, incremental changes to their financial plan—as well as product research and analysis capabilities, will provide mass affluent clients with further confidence to make their own investment decisions, and take action on nudges and insights.

Meanwhile, as mass affluent portfolios become more complex, customized client statements and 360-degree portfolio views can give clients a sense of personalized visibility into their investment positions and financial outcomes.

Driving Continuous Improvement Through Tracking Engines

For software-driven companies, continuous deployment has become a crucial strategy for automatically delivering and testing code. To serve mass affluents at scale, wealth managers need to create a similarly agile data-to-deployment cycle.

AI and machine learning tools can allow wealth managers to continuously improve sub-segmentation. Sub-segments should never be static; rather, they should be constantly adjusted based on the performance of sub-segment-specific engagement campaigns to drive positive business outcomes.

None of this is to say that financial advisors will be forced to re-train as AI experts or big data analysts. Using a robust tracking engine, data experts (AI consultants, data scientists and data engineers) can work hand-in-glove with domain experts who understand the intricacies and trends of the relevant customer challenges from a wealth management standpoint. As it ingests massive amounts of customer feedback, the tracking engine will drive continuous improvement. Intuitive dashboards designed for wealth management and marketing teams—rather than for data experts—are critical on this front, in order to take into account customers’ multiple relationships with the bank or firm and inform new campaigns and testable hypotheses. 

Optimizing Onboarding

While not a hyper-personalization tactic in and of itself, optimizing the onboarding experience is crucial when it comes to building a UX that feeds seamlessly into a hyper-personalization strategy.

A mass affluent’s first point of contact with a wealth management firm is likely to be a website or an app rather than a handshake with their investment advisor. To compete with fintechs, wealth managers need to onboard new clients as seamlessly as possible. Even large institutions can further improve their performance with mass affluents by focusing on customer onboarding.

Both the UX improvements and the workflow efficiencies of an optimized digital onboarding journey will supercharge a larger hyper-personalization strategy aimed at mass affluents.

A Unified Strategy: From Automation to Productive Hybridity

With a hyper-personalized advisory model, wealth management firms can remain competitive with mass affluents and even outflank new industry incumbents that rely more totally on automation. The winning strategy will not be automation at all costs, but rather smart automation, using intelligent sub-segmentation to apply human touchpoints when and where they can have the greatest impact on clients and their wealth management trajectories. By capturing mass affluents early in their financial journeys, wealth managers will also be able to position themselves as the firms of choice for the HNIs of the future.

Finally, while a hyper-personalization strategy should always be customer-driven, one fringe benefit should not be forgotten: A hyper-personalized wealth management solution aimed at mass affluents will also be transformative for wealth managers themselves, equipping them with digital tools to better understand all clients (not just mass affluents) and act on those powerful new data insights. Rather than diminishing the role of wealth managers, hyper-personalization strategy aims to scale their impact, allowing them to serve more clients and grow their advising practices with digital tools.