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 "
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
There are some financial institutions that are deploying digital banking platforms that are built on data analytics engine that
Even if a bank or credit union has not yet made the move to