Disrupting wealth management
The age of hyper-personalization
How wealth businesses are shaping themselves to provide customers with personalized financial advice to meet their high expectations.
For businesses, exceptional customer experience is a central concern, a strong brand differentiator, and, ultimately, a competitive advantage. In the retail sector, for example, customized services and supplier feedback—such as product suggestions from Amazon, movie recommendations from Netflix, and playlist offerings from Spotify—drive consumer loyalty. Similar client-focused practices have taken root in wealth management, with more and more customers demanding the same highly personalized and individually tailored services from their financial advisors. The result of this trend is hyper-personalized wealth management, or hyper-personalized advice, wherein advisors provide services tailored to their clients’ high expectations. However, in trying to keep up with the nuances of each client—and often already juggling a full portfolio—these professionals are constrained by time and other resources. Offering advice targeted to a specific area of a client’s needs versus to the overall financial picture—that is, scaling advice—is already a challenge for wealth managers; hyper-personalization at scale seems insurmountable.
But without this level of tailored advice—and awareness of the ensuing perceived value in advisor services— investors are quick to move to lower-cost providers such as robo-advisors. It’s thus become imperative for wealth-management firms to elevate their offerings in order to help maintain and foster strong and engaging client-advisor relationships. HSBC anticipates that this shift will have large-scale effects on the banking industry in terms of customer service; this likely will also hold true for the sector’s investment services. As HSBC representatives explained in a November 2019 article in business magazine CDOTrends, “In the future, customers will increasingly expect a highly personalized service determined by their individual requirements, instead of based around a set of savings, borrowing, and investment products, each with their own sales and servicing characteristics.”1 As further surmised in the article, advisors must make use of customer data, analytical technology, and Artificial Intelligence (AI) in order to hyper-personalize their offerings and services, and build trusting—and lasting—client relationships.
Personalization can be defined as tailoring services according to a person’s affiliations, such as those to a particular social group, income bracket, or segment of like-minded people, or personal likes and dislikes. Companies typically use this technique to create and strengthen brand loyalty in consumers. Hyper-personalization—increasingly in demand—takes it one step further, providing a truly unique, engaging, and highly customized experience to the individual consumer. In fact, according to a 2016 Salesforce survey, 51 percent of consumers expect companies to anticipate their needs and offer relevant suggestions, even before first contact.3 Unsurprisingly, with the immense impact hyper-personalization can have on generating revenue and attracting and retaining customers, companies are rushing to the table. Moving beyond the previously noted levels of personalized recommendations offered by Netflix, Spotify, and Amazon, Starbucks has crafted its rewards program as a means by which it can offer hyper-personalized suggestions. Combining a user’s location data and past purchase history, the program can then offer tempting, tailored promotions.2 Clearly, not only must banks and wealth-management firms supply personalized experiences to keep up with other service providers, they must do so to meet client expectations—and supply hyper-personalized offerings to exceed these expectations. But to do so, advisors must truly understand and embrace each client’s inherent complexities—including specifics related to family, work, and community—in order to appreciate their current financial status and unique goals. This, in turn increases advisors’ ability to connect the dots, contextualize their advice, and ultimately provide highly tailored recommendations. With a deeper level of understanding, wealth managers can then be equipped to offer advice that extends beyond solely the financial, tapping into more detailed aspects of clients’ lives—such as their current health and career status, as well as their larger personal and professional interests. Getting to know an investor in a broader context gives advisors the opportunity to move beyond the brief, impersonal conversations that typify customer exchanges, and instead engage in earnest discussions that help build trust and, ultimately, long-term client relationships. But for advisors to be able to operate on this deeper level, their wealth-management firms must make company-wide commitments to hyper-personalization.
When that occurs, advisors are free to offer highly tailored, fine-grained advice on how clients should manage their hard-earned money. Systems that incorporate technology and data science further add to the tool kits with which wealth managers can tailor their services: the comprehensive collection and use of customer data can help equip advisors with a 360-degree view of their clients, while the fundamentals of behavioural science and advanced analytics and AI can lead these professionals to highly nuanced, actionable insights, all of which can benefit individual investors. Delivering hyper-personalized advice on a consistent and timely schedule, then, becomes an overall positive feedback loop, allowing advisors to proactively engage with their clients and build on the trust they’ve established. Continued over decades, advisors can help develop powerful predictive profiles for their clients—these profiles might include, for example, personal decision-making patterns and strategies for identifying short-term objectives.
In a June 2019 Temenos and Forbes Insights survey of wealth-management executives, an overwhelming 82 percent of respondents believed that those who increase product personalization will succeed.4 And in a recent Refinitiv survey of wealth-management executives worldwide, 61 percent of respondents rated the use of analytics and the ability to create insights as “very important,” with 39 percent considering these as “important” for their firms over the next 12–18 months.5 In the same vein, top wealth-management firms are embracing the need to provide highly personalized services and are developing and purchasing tools that will allow them to do so. Morgan Stanley has developed a system, named Next Best Action (NBA), that generates highly personal recommendations that wealth managers can take to their clients. In the first two months of the COVID-19 pandemic, the company’s advisors used their NBA system more than 11 million times, spiking productivity and leading to an average increase of five to six customer calls per day. The corporation’s wealth-management chief analytics-and-data officer subsequently noted that it used to take about 45 minutes to come up with a personalized investment idea for a client, but with the NBA system, the process is now instantaneous.6
Each client has unique investment preferences, ethical beliefs, life goals, and risk profiles. With hyper-personalization, portfolios can be individually tailored to these specifics. As a great example of the extra value that hyper-personalized services can bring, consider a targeted asset allocation for those who prefer responsible investing: With these tailored environmental, social, and governance (ESG) profiles, clients get the opportunity to direct their money to entities whose world views and beliefs closely align with their own. Globally, the percentage of both retails investors and institutional investors that apply these ESG principles to at least one-quarter of their portfolios increased from 48 percent in 2017 to 75 percent in 2019.7 This jump was partly a result of advisors more precisely aligning clients’ investments with their specific social-issue interests. One of the many firms employing an ESG-based practice is Ethic, a technology-driven asset-management company that creates sustainable investment portfolios—e.g., those that allow for growth amid environmental improvements. The company does this by using separately managed accounts optimized to track the market according to the sustainability criteria determined for each client. These criteria, in turn, are predicated on a spectrum of ESG issues. In addition to offering sustainable investment portfolios, Ethic has also seized another opportunity to provide clients with insight into the value of their specific holdings, via personalized customer reports. But even without these types of value-added reports, tailored ESG profiles helps foster emotional connections between investors and their wealth managers. And in the long run, this translates into increased trust within the client-advisor relationship.
Unlike generalized wealth advice, hyper-personalized services rely on access to broader types of customer data than are traditionally available to advisors. Client information normally shared during initial contact—including age, marital status, and financial standing—and via direct exchanges alone is simply not enough to drive hyper-personalization. So, advisors must obtain and understand customer specifics such as individual personas, values, beliefs, behavioural and transactional data, client-engagement data, customer potential, and mobile data. But determining how to acquire and use this detailed information can present a challenge. This hurdle applies across the spectrum of client segments, from small individual investors to large-scale high-net-worth (HNW) clients. But from the data insights generated by systems built for hyper-personalization, advisors can combat what might otherwise be distracting information overload, and ultimately provide the investing options that are most relevant to their clients. One business that openly promotes its personalization services as a value to clients is NexJ Systems. This company aims to revolutionize customer-relationship management, using AI, machine learning (ML), and Natural Language Processing (NLP) to build holistic, 360-degree client profiles. With its value-added customer interactions and personalized financial services, NexJ seeks to offer effective, timely recommendations in order to prompt advisor interactions with clients—thus further building client trust. Similarly, with Deloitte’s Acquisition.AI, the client-acquisition process gets the hyper-personalization treatment. This program uses a rich set of third-party data, market research, and mobile data to generate detailed information about a multitude of customer segments, which can then be further personalized to tailor marketing efforts to customer acquisition.
To achieve a truly hyper-personalized service, however, organizations need to be deliberate in the data they obtain and the processes through which they draw insights from it. Fundamental to this is the ability to identify and acquire valuable and relevant customer-centric information, and then to apply it to companies’ existing data platforms in near real time. The data must then be interpreted and grouped so that advisors can obtain 360-degree views of their clients, thus helping them to offer timely, informed advice. Some common sources of valuable client data include financial and personal information—e.g., family, occupation, and community affiliations—details about personal aspirations, particulars of advisor interactions and engagement—e.g., contact frequency, purpose, method (e.g., email), and even overall time span of the client-investor relationship. Less traditional sources of investor information can include social media, location data, and behavioural and transactional data.
With powerful resources such as behavioural sciences, advanced analytics, AI, and ML at their fingertips, wealth-management firms are better positioned to detect client patterns, draw insights about otherwise complex and highly nuanced investor profiles, and identify short- and long-term customer objectives. All this allows advisors to plan their counsel more effectively. Complex technology-driven customer-centred models, such as Morgan Stanley’s NBA personal-recommendation system, have been shown to elevate advisors’ ability to serve their clients. Other such models can be made to estimate client complexity and/or assets held outside of an advisor-directed portfolio—information advisors can then use when recommending additional and/or complementary products. Firms can also build propensity models—which use past data to try to predict future behaviour and trends—to identify clients at risk of fleeing to a competitor, so that wealth managers can proactively engage them before they sever the relationship and transfer their assets. But amidst all the novel customer data and technology, it’s important to remember that advisors are nevertheless essential to hyper-personalization. Training wealth managers so they’re comfortable with both the new technology and the new culture of data-driven client interactions, and redesigning existing information systems so advisors can continue to use them when dealing with investors, will all help drive this new era of hyper-personalization.
Highly tailored advice will soon be the low bar for client value. Clients increasingly expect their wealth advisors to have an intuitive understanding of their financial and personal situations, and increasingly want advice customized to their preferences and goals. That’s why organizations need to invest now in the data, analytics, and AI initiatives that will help them to offer targeted advice and follow-through—that is, help them provide coveted hyper-personalized experiences for their clients.
Chelvan Sivayogapathy, CPA, CA
1. “HSBC sees hyper-personalization in banks’ future,” CDOTrends, November 14, 2019, https://www.cdotrends.com/story/14529/hsbc-sees-hyper-personalization-banks%E2%80%99-future.
2. Kate Taylor, “Starbucks is rolling out a new system to convince you to buy exactly want it wants you to buy,” Business Insider, December 7, 2016, https://www.businessinsider.com/starbucks-develops-personalization-system-2016-12
3. Devon McGinnis, “Please take my data: Why consumers want more personalized marketing,” The 360 Blog, Salesforce, December 2, 2016, https://www.salesforce.com/blog/consumers-want-more-personalized-marketing/.
4. “Empowered by technology, wealth managers will take personalization to the next level,” Temenos and Forbes Insights, June 17, 2019, https://www.forbes.com/sites/insights-temenos/2019/06/17/empowered-by-technology-wealth-managers-will-take-personalization-to-the-next-level/#35f8cebe38c4.
5. “Digitalization of wealth management: Client-centricity through personalized wealth management,” Refinitiv, February 14, 2020, https://www.refinitiv.com/perspectives/ai-digitalization/client-centricity-through-personalized-wealth-management/.
6. Tom Davenport, “The future of work now: Morgan Stanley’s financial advisors and the next best action system,” Forbes, May 16, 2020, https://www.forbes.com/sites/tomdavenport/2020/05/16/the-future-of-work-now-morgan-stanleys-financial-advisors-and-the-next-best-offer-system/#221f100b7027.
7. "The ESG global survey 2019,” BNP Paribas, https://securities.bnpparibas.com/global-esg-survey.html