With today’s technology, advisors can segment investors with ease and deliver “mass-customized” sales and service approaches tailored to different groups. For example, in a recent research engagement, Broadridge and Bridgeable synthesized data on investors’ behavioural and psychographic tendencies, evaluating key characteristics like preferred portfolio management style (advisor-guided or DIY), personal communication values and preferences, and information-seeking style (high touch or low touch).
Based on the results, the firms identified four overarching archetypes of investors: Self-Sufficient Researchers, Support Seekers, Habit Keepers and Busy Collaborators. Investors in each archetype share a unique set of traits that reveal bespoke preferences when it comes to portfolio management and how much information they want from their advisors and how often. With technology tools such as AI and machine learning, advisors can better understand how their clients fall into these four archetypes and tailor their communication and correspondence accordingly; ultimately lead to more personalized experiences and financial advice, creating a bespoke financial planning experience.
AI and machine learning to grow existing client portfolios
According to a recent study by Broadridge, about half of wealth managers who have made significant investments in AI have already increased revenues as a result. As such, Data, AI and ML are presenting even bigger possibilities with existing clients by allowing wealth managers to “hyper-personalize” their service strategies.
AI and ML applications have the ability to “learn” about clients by analyzing data like past transactions and product use, and by monitoring clients’ digital footprints—tracking where and when clients click, what they watch and how much time they spend on any individual piece or type of content. These solutions reveal clients’ interests, likes and dislikes, providing advisors with invaluable insights about how best to serve individual clients.
This deep pool of data can be used to feed predictive analytic solutions that anticipate client needs and make suggestions about how best to meet them. Because the AI and ML technology is constantly “learning” more about the clients, these suggestions get better over time. For example, some AI solutions available today have proven accurate enough to predict account closures and large asset withdrawals. Some applications can flag an at-risk account as far as 90 days in advance, and automatically suggest steps the advisor can take to address the client’s specific needs and retain the account.
