Identifying Gen Ys Before They Become Emerging Affluent

Millennials.jpg Editor's Note: In part 2 of our blog series discussing artificial intelligence, robo roles, and analytics, we discuss in depth the Gen Y market. As Gen Y continues to enter the workforce and gain spending power and weatlh, financial institutions can use tools such as AI, PFM, big data, etc. to target this segment and gain vital market share.

In the last post, we covered the fact that Gen Y is set to become the largest percentage of the U.S. population in 2020, and then by 2025 spend 50 percent more a year than Gen X and control almost half the nation's wealth. Understandably, this makes Gen Y a coveted target for providing a variety of financial management tools, especially to those among the demographic who are emerging affluents. The trick, as noted previously, is finding them before they are emerging and affluent.

The firs step toward this goal seems obvious - i.e., identifying characteristics within this target group that indicate a movement toward increasing wealth. In some ways, there is nothing new under the sun regarding targeting parameters to be used for this purpose. Holding a college degree, working in a profession and having an annual income that is approaching six figures are positive indicators of a path toward affluence for most generations, including Gen Y.

However, there are some ways that Gen Y emerging affluents differ from those of other generations. A larger percentage of members of Gen Y moving toward affluence will be females. Marriage percentages will be lower in general as Gen Y members are waiting later to marry and start families. This could mean that this group will build investable assets sooner in life and because Gen Y has an average life span longer than any other generation, their investing horizon naturally is longer. This means the low end of the investable asset range for the soon-to-be affluent in this generation may be lower than has been seen previously.

The second step, to determine which Gen Y customers or members fit the above profile is much trickier. Basically, a financial institution has two options: 1) Cast a wide net using Gen Y's age parameter (25-34 years) then continue to drill down into those caught in that net, or 2) deploy a data analytics capability across all digital channels that captures detailed information about the financial situation of all users of those channels.

For many banks and credit unions, if not most, the wide net approach is their only option. Even if they have a PFM offering, that offering most likely requires an "opt-in" by the end users. With adoption rates for PFM stuck around 18% across the board, the view provided by them of this segment within Gen Y is likely very limited. A possible strategy for mitigating that is to mount a campaign targeting the age demographic for Gen Ys with an offer/incentive for them to use an institutions PFM services (assuming those services include auto-categorization and enhanced data visualization). Those who accept the offer (and by default opt-in to allowing the PFM system to access their financial information) would be worth further investigation comparing them to the desired demographics.

Another option would be to apply artificial intelligence (AI) tools, e.g., robo report writing, to find potential customers or members who are approaching the emerging affluent category. The use of the full range of AI tools applicable to providing financial services is inevitable for banks and credit unions wishing to remain competitive. Robo report writing would be a first step to test the value of such adoption. An institution could feed into a robo report writing tool's basic information about the Gen Ys it serves (e.g., number of accounts, types of accounts, average balances, services used, etc.) using the age range for the target group and add big data available about the general habits of the emerging affluents in that generation. The resulting report could be sent to the same individuals identified with the 25-34 age range using basic marketing automation tools to identify and further understand who opens and reads the report.

There are some financial institutions that are deploying digital banking platforms that are built on data analytics engine that does capture detailed financial information about all digital users, though the number outside of the TBTF club is small. This strategic approach to understanding the needs of all customers and members offers many more options, not only for identifying Gen Ys on their way to being affluent but also for performing the same query (and many others) across an entire client base.

These data driven platforms combined with robo report writing tools would allow financial institutions to more specifically identify appropriate targets within larger groups such as Gen Y. In addition, the report created would be able to provide views and comparisons that utilize data about the recipient unique to them. Imagine the impression made by sending technology savvy Gen Y prospects a report containing baseline data that reflects their financial position to the penny. 

Even if a bank or credit union has not yet made the move to data driven digital banking platforms that can deliver this kind of powerful, actionable analyses, it is imperative that these institutions begin now to investigate what can be done with emerging technology options offered by AI in its many forms. Rest assured, brokerages with their full set of investment and banking services are already well down this road. There is time to catch up but it is not infinite.