As baby boomers transfer their wealth to their Gen X and millennial children, the mass affluents combined will grow significantly and shift to younger generations. Members of this rapidly growing class of investors hold investable assets from $100K to $1 million. This transfer of wealth will create a significant opportunity for financial advisors to connect with younger investors.

Wealth managers could take advantage of the opportunity by not servicing mass affluent and lower-end HNI clients, who reportedly drive more than $230 billion in revenue.

As mass affluents desire more personalization in their financial advisory services, serving them is all about efficiency. Firms providing wealth management services explore several tactics to serve the mass affluents at scale. Many have turned to digital solutions like robo-advisors, automating risk/reward surveys, and portfolio rebalancing function with mixed results. 

We will cover driving factors and use case examples of hyper-personalization in a financial advisory to help the advisors gain more expertise in the field.

Driving Continuous Improvement Through Tracking Engines

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

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

However, none of the above implications specify whether 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 solutions standpoint. 

The tracking engine will drive continuous improvement as it ingests massive customer feedback. Intuitive dashboards designed for wealth management solutions and marketing teams, rather than for data experts, are critical to consider customers’ multiple relationships with the firm and inform new campaigns, including testable hypotheses. 

Engaging Mass Affluents with Hyper-Personalized Services

Wealth managers who want to win the mass affluent segment must synthesize different tactics into a unified hyper-personalization strategy.

New digital capabilities 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, and tracking engines. The models will use data-driven insights to hyper-scale wealth manager expertise, and seamless new onboarding experiences.

Innovative wealth managers are optimizing their customer journeys through gamification, providing clients with specific products and goals that sync with their sub-segments. They also try to understand the client’s current life situations and issue rewards while engaging with the products and completing different goals. Nudges and actionable insights can surround the gamified features with sound, data-based recommendations.

Hyper-personalization needs to feel open-ended on the client’s side rather than overly prescriptive. That is why goal-based planning and portfolio rebalancing tools are a must-have in a wealth management solution or platform. They function like an in-app calculator that helps clients visualize the impact of small, incremental changes to their financial plans, including product research and analysis capabilities. The tools will further provide mass affluent clients 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 organizations a sense of personalized visibility into their investment positions and financial outcomes.

Optimizing Onboarding

Onboarding optimization may not be a hyper-personalization tactic in itself. However, the 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 firms providing wealth management services is likely to be a website or an app rather than a handshake with their investment advisor. To compete with fintech, wealth managers need to onboard new clients as seamlessly as possible. Large institutions can improve their performance with mass affluents by focusing on customer onboarding strategies.

Finally, a hyper-personalization strategy should always be customer-driven. However, it should not avoid considering one fringe benefit which is the hyper-personalized wealth management solution. It is aimed at mass affluents and will also be transformative for wealth managers themselves, equipping them with digital tools to better understand all clients apart from mass affluents, and act on different powerful new data insights. 

The hyper-personalization strategy aims to scale the impact of wealth managers rather than diminishing their role, allowing them to serve more clients and grow their advising practices with digital tools.

Use Cases as Examples of Hyper-Personalization 

In one of its recent projects, Wipro worked with a large retail and commercial bank in the UK, struggling with low referral rates and high dropout rates. By simplifying the customer onboarding journeys and building more robust reporting mechanisms, they were able to increase their conversion rate by 51% and lower the dropout rate by 5x. The bank was also successful in eliminating manual and expensive data entry by personal wealth advisors.

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

In another significant project with a large bank in the Middle East, Wipro 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.