Late in 2016 Deloitte published a fascinating market profile of the millennial generation, basically anyone born between 1980 and 2000.
Considered as an important demographic, millennials will be the largest adult segment by the end of the current decade. That’s three years from now. And not just in the West: Already nearly two thirds of Asians are millennials.
Millennials are entering their peak years for earnings, consumption and investing. They are coming not just in numbers but with documented differences in the way they think about money.
From where I sit, the financial services industry doesn’t know how unready it is for the millennial wave to break.
Companies in every industry commonly hire for the operating environment that is about to expire. Everyone concerned relies on their expert judgment of talent. It is a judgment formed in the old world and so they fish in the old talent pools. And they hire the wrong people.
In an age of talent shortages this costs them money and constrains growth — not just future growth but growth right now.
Recently Wholefoods, the big US grocer, conducted a search for a senior HR executive who would “hack our people practices.” Grocery retailing is far removed from the clients we serve at The Options Group, but I know exactly what Wholefoods is after.
I’d like to see a similar kind of impulse among financial-services firms to get themselves ready for the long wave of millennials coming down upon them.
Here are some big ideas for doing just that.
As they enter the most dynamic phase of their adult lives millennials will be building college funds and retirement nest eggs. And they will be looking for ways to be self-sustaining to an unparalleled degree. More than half plan to start new business. More than a quarter already have. To a degree unprecedented in history they will have inherited wealth as their baby boomer parents pass from this earth.
But bear this in mind: Millennials appear to be generationally cautious, and with reason. Terrorism and financial crises have occupied a large part of their lives. According to Deloitte, as a group the demographic is leery of stocks (which are less than a third of their net worth). They are attracted to alternative investments — whatever those are. Millennials, according to Deloitte, acknowledge a broad lack of basic financial knowledge.
Given what appears to be a generational predisposition toward caution, trust will be an essential quality in millennial relationships with their financial-services firm. The capacity for building trust beyond conventional fiduciary obligations will require formation of authentic personal relationships different, perhaps, from the established conventions of what we’ve learned to think of professionalism.
Millennials are digital natives. They are not merely comfortable with technology. They assume technology. The implication for financial firms is that they will need to recruit for skill sets, naturally, but also recruit personalities who can simultaneously build relationships and think in innovative ways about investment products. Successful financial talent for the next 30 years will understand the intersection of technology and investing, certainly, and be simultaneously adaptable to the challenge this intersection poses to the industry’s conventional revenue models.
This is a new talent profile for the financial-services industry, one most of the industries managers are not (yet) accustomed to recruiting. Ready or not, the industry is about to join the world of Big Data and talent analytics. And not a moment too soon.
Breaking with convention
The promise of Big Data solutions like predictive analytics does not lie in collecting information. That’s the least of it. The promise lies in using advanced statistical techniques to find previously obscured patterns and uncover hidden value.
Recently, for instance, a large financial-services client asked my firm to add assessment of cultural fit to our search activities on its behalf. It is a comparatively easy modification of our intake process that we’ve done before. It allows us to sort candidates better and faster, and focuses the client’s attention on metrics aside from job history. It is a kind of baby step toward predictive analytics.
The conventional recruitment process is full of noise, to use a term of data scientists. The noise comes from resumes, job boards and hit-or-miss personal references. Then there’s human element — the interviewers. The interview process trusts in serendipity to an unnerving extent.
It’s no secret that humans have a hard time with objectivity, and that even the most modest among us value our own opinion more highly than we should. It’s well documented that even “experts” are susceptible to conscious and unconscious filters — and not the obvious prejudices either, like race and gender. It’s been demonstrated again and again that the long-term unemployed, for example, make committed employees and yet the prejudice against them persists. Humans like to make quick decisions, after all. How much simpler to check a CV for a brand-name university and an uninterrupted job history.
Going in the direction of growth is harder when the experts running things all understand the world in pretty much the same way. My experience of the financial-services industry is that is a closed network in which the same strategic world view and the same measures of success are agreed upon. Management teams reflexively replicate the current generation of industry leaders in the hiring process. As they do with the suits they wear they prefer talent with a traditional fit. They hire the familiar. They hire themselves.
Just compare the profiles of leaders at fintech firms with those of established Wall Street investment bankers. I have, and the differences in background and personality type is stark. We find it hard, to put it plainly, to see beyond our own reflections in the mirror and spot what may be nontraditional candidates but who are exactly who we should be looking for if only we knew it. That’s where talent analytics will be transformational.
The promise of talent analytics
A conventional CV or a LinkedIn profile is a list of jobs held and skills acquired. By harvesting such lists every organisation of any size has built a process for culling and tracking applicants. These processes might be called beginners analytics.
Lists of credentials, though, do not capture signals about talent and future performance — qualities like motivation, cultural fit, learning style, comfort in collaboration and the distinctively human capacity for resilience and persistence. A CV or a LinkedIn profile won’t surface predictive variables that none of the parties involved even realises is a predictive variable. For example, Gallup discovered that a group of military trainees in a particular unit were 1.5 times more likely to complete a rigorous training program if they had a friend or family member who had served in the unit.
One day soon talent analytics will be understood as a profoundly strategic tool. It is not hard to imagine, for instance, “talent modelling” with an analytics tool to directly address strategic questions like the probability of success for specific kinds of talent under different kinds of market risk. Or the right match of talent to alternative futures for an organisation — the investment patterns of millennials, say, versus those of their parents.
Predictive analytics are not yet commonplace even in large organisations. Right now they are largely a Big Data phenomenon. To justify their cost they are likely to be available only for large scale search needs — for the present. It is not hard at all to imagine a day sometime soon when even small firms have access to pools of raw data and the software for finding predictive patterns in it. When that happens talent analytics will become a commonplace feature of the search and recruitment process. Analytics will not be a standalone tool but one married to the human genius for framing problems and pursuing answers.
Predictive analytics will never wholly remove human wisdom from the talent-management algorithm, nor should they. Any algorithm is only as good as the questions it was developed to answer. Asking questions is what humans do best. Questions like, “How do I serve a demographic different in important ways from any I’ve ever served before? A demographic that’s going to dominate my business for the next 30 years?”
Richard Stein is chief growth officer at Options Group in New York.
 Millennials and Wealth Management: Trends and challenges of the new clientele. Kobler, Hauber & Ernst. Deloitte Touche Tohmatsu Limited (UK).
 Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. Paul E. Meehl. University of Minnesota Press, 1954. Cited in “Minds and Machines: The art of forecasting in the age of artificial intelligence.” Guszcza & Maddirala. Deloitte Review. Issue 19. July 25, 2016.
 Hiring Decisions: Big Data Isn’t Enough. Rigoni & Nelson. Gallup Business Journal. February 25, 2016.