Cluster analysis is a statistical method of grouping people based on shared similarities: gender, ethnicity, age, where they live, and so on. Although there will always be exceptions, this “profiling” generally provides a relatively accurate picture of the needs of different employee groups because it’s based on actual data. As you start to understand statistically significant segments of your population, you can then think of targeted strategies that speak to each group, rather than speaking to your employees as a homogenous whole. Then you get more engagement and utilization of benefits, and you can make a real change in your employees’ financial wellness.
55% of Americans experience income that fluctuates more than 30% in income on a month-to-month basis 1
We did a cluster analysis project with a large multinational retailer, in which we performed statistical modeling to identify which factors influence financial wellness behaviors. We looked at the employee population in terms of ZIP code, ethnicity, and gender. We look at ZIP codes because people tend to flock toward other people like them. The value of the homes in the area, as well as whether it’s urban, rural, or suburban, are all factors — people who live in the same area tend to have similar financial behaviors. The same goes for ethnicity. Group behavior rubs off on other people and values are passed down through generations. Although it would be wrong to draw any sweeping generalizations, this kind of analysis is helpful, as it gives you a baseline for understanding where your workforce is financially and what it needs, as well as how to best communicate to different groups.
Interestingly, after accounting for these factors as well as actual earnings, tenure with the employer, and what part of the company employees worked in, we found that the strongest indicator of an employee’s 401(k) contribution rate was his or her supervisor’s contribution rate. The second strongest was his or her peers’ contribution rate.
1 JP Morgan Chase, Paychecks, Paydays, and the Online Platform economy (2016)
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