Robo-advice needs right numbers for that personal touch

Craig McCulloch


March 7, 2018

Automated advice promised to bring high quality to the masses by focusing on processes and products.

This solved a problem for the industry, not for customers. And that makes it a hard sell.

So far, few automated advice companies have attracted significant funds under management. Those at the forefront, such as Betterment and Vanguard, have either pivoted to include face-to-face advice or are using strong, traditional distribution channels.

The reason is simple. The focus on process (typically using relatively simple advice algorithms) and product (often via portfolios of exchange-traded funds) across the automated advice industry missed the element that compels customers to shell out their hard-earned money for advice: a deep emotional attachment to their money and future, which is best maintained through a trusted relationship.

This fact points the way towards the opportunity: automated advice needs to connect better with customers through engaging stories that reflect their lives. This requires intelligent use of big data and holistic, goals-based algorithms that ultimately lead to the right solutions for the customer’s specific needs at the end of the advice chain.

Sensible comparisons, easy to understand

A simple example is super fund advice that predicts a member’s retirement balance based on various inputs such as contributions, current balance and investment strategy, then compares that balance with what’s required to have income equal to the Association of Superannuation Funds of Australia Retirement Standard. It’s simple to produce, but for most people the results don’t relate to their personal circumstances.

Milliman’s retirement expectations and spending profiles (ESP), which analyses the real-world spending of more than 300,000 retirees, shows that ASFA’s Comfortable Standard equates to about the top quartile of expenditure by actual current retirees.

Sensible comparisons of people’s lives and lifestyles, presented in language they can understand, mean far more than arbitrary targets. The data captured by the Retirement ESP can also produce individualised inputs for goals-based algorithms that become the basis for meaningful, engaging advice.

The difficulty in building such algorithms shouldn’t be underestimated. The Australian Securities and Investments Commission issues stringent guidelines for how advice algorithms should be monitored and tested, which can be costly and difficult compliance for cash-strapped start-ups.

Change at the big end of town

It is not an easy path but we are seeing forward-thinking, large organisations make the effort. In some cases, it’s leading them to tear up their traditional advice and redefine its underlying nature.

They’re using big data to make advice personal and analytics to make it about the customer’s specific circumstances. They’re investing in real innovation that engages the customer.

This is a far more engaging path than hitching your organisation to process-based, product-centric vehicles that have failed to gain traction around the world.

Automating processes, building efficiencies and developing scale all have their place across the advice industry. But that place is currently behind closed doors, with experienced advisers communicating the output to their customers. It’s the strategy that resonates with customers seeking a service tailored to their individual lifestyle and aspirations. In the eyes of customers, pure automated advice may still be seen as impersonal, less resonant and less likely to tug at the heartstrings.

Organisations that succeed will use technology to change the way they have emotionally engaging conversations with people. At their heart, smarter algorithms and better use of data enable organisations to talk to people about things that matter to them.

What conversation are you having?

Craig McCulloch is a principal at risk and retirement firm Milliman. Craig will be speaking at the Conexus Financial Post-Retirement Conference.

TOPICS:   robo-advice

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