The Advisor’s New Crystal Ball

Peering into where clients work, live, and play, a new era of prospecting has RIAs using data like never before.

(Illustration by II)

(Illustration by II)

Financial services firms have traditionally looked outward to attract new clients. But Wealth Enhancement Group, an independent wealth management firm that oversees $11.8 billion in client assets, has flipped that approach on its head.

The Minneapolis-based firm examines data from existing clients that advisors know are happy with their work to identify common characteristics, such as what they do, where they live, and what activities they are involved in. Then, the firm targets its marketing efforts towards other households showing similar behavior to increase the chances of finding new clients.

“Using analytics we can start to predict people we know have a higher probability of liking what we do,” says Jeff Dekko, chief executive. “Which means there is a higher probability that they will become a client – and a higher probability that they will be satisfied with the service.” Last summer, the private equity firm TA Associates announced that it was acquiring a majority stake in Wealth Enhancement Group, which is affiliated with LPL Financial, from Lightyear Capital LLC.

Investors and advisors alike have become accustomed to having their lives made easier by predictive data. People on both sides of the RIA relationship interact daily with the likes of Microsoft (LinkedIn), Twitter, Apple (Siri), and Amazon (Alexa). Their platforms offer convenience and ease of use. And increasingly, they will know what song, movie or job might hold appeal before even being asked.

But only the most advanced RIAs are starting to tap into the potential of predictive data, according to Frank Coates, executive managing director and co-group president at Envestnet Analytics. In May, Envestnet released a suite of tools on its Envestnet Intelligence platform, which includes artificial intelligence and natural language processing. “We have hundreds of clients using analytics, but only the most advanced are using predictive analytics,” Coates says.

“The companies that are the early adopters of predictive and other natural language processing tools tend to have leadership that is technically driven and the ones that are slow adopters tend to have legal, business, or compliant-driven organizations,” Coates says.

This year, Envestnet will offer a tool that takes what has been learned in predictive modelling and uses that data to offer advice. The platform will be able to crunch data around planning, tax, risk, and asset allocation and link this with outside accounts showing spending and debt data, enabling the firm to project what the next five or ten years might look like. Coates says this kind of data will help advisors to predict, before they have been told, when a client might want advice about a home purchase, new car, or education plans.

Predictive analytics use historical data to identify patterns and forecast future activity, behavior, and trends. The global predictive analytics market was valued at approximately $3.5 billion in 2016 and is expected to reach $10.95 billion by 2022, a compound annual growth rate of 21%, according to Zion Market Research.

The finance and risk sectors have emerged as the fastest adopters of predictive technology, with over 40% market share of total revenue generated across the predictive data industry in 2016.

Tej Vadka is a senior leader in global capital markets at Capgemini, where he helps financial service firms identify innovative solutions to improve operations. He says predictive data will become pivotal to providing customized advice, but that getting there will be a two-step process: “The wealth management firm will have to first empower the advisor, so the advisor can serve the client.”

These solutions range from add-ons to a smaller advisor’s existing CRM or more customized solutions for big wirehouses. Among the bigger solutions, Capgemini’s Augmented Advisor Intelligence platform uses artificial intelligence to determine where an advisor’s strengths lie and where they can improve.

An Asia-Pacific wealth management firm cited in Capgemini’s World Wealth Report 2019 uses data to match the personality, lifestyle, and behavior of prospective, and current, clients with a compatible wealth manager.

Having access to granular performance data will likely become increasingly important when matching investors with advisors and points to a large opportunity. In 2018, only 55.5% of high net worth individuals said they connected very well at a personal level with their wealth managers, Capgemini research found. At the same time, half of those investors showed an interest in wealth management services provided by big tech firms.

Investor appetite has inspired wealth management firms to invest in emerging technologies such as artificial intelligence to stay ahead of the competition. Morgan Stanley, for example, has introduced a predictive system that includes content on life events. This unique system enriches the relationship between the client and the advisor by recommending suitable services and financial strategies to deal with family illness, for example, or a change in circumstances like divorce. Jeff McMillan, chief data and analytics officer, says that these tools are meant to complement the human relationships between advisors and clients, rather than replacing them.

However, data must be handled carefully so that advisors don’t fall foul of privacy regulations or accidentally discriminate based on historical patterns. Firms could end up losing customers if they are seen to overstep boundaries. While Envestnet deals with RIAs, rather than their clients, it still treads carefully around collecting client data from publicly available sources, like Google and LinkedIn, Coates says. “We will work with that data but only if it’s collected. We’re never going to take the lead about collecting that data, even though U.S. regulation is not quite as advanced as GDPR in Europe.”

Wealth Enhancement Group, which has used a Salesforce platform to process data since 2015, says that once the firm has started working with a new client, it aggregates publicly available data, including what kinds of things the client has searched online, what events they have attended, what has been discussed in meetings. “We’re measuring all the way through,” Dekko says. “And we correlate that with our predictive model.”

Correction: A previous version of this story indicated that Google offered Siri.

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