In the era of big data, information - even the seemingly mundane - has more value now than it may ever have had before. Living in modern society and interacting with the internet has given most people a first-hand window into just how many data points we each leave behind in our wake and how those histories can be collected, analyzed, and used in a predictive capacity - sometimes with unsettlingly accurate results.
In the business world, collecting as much data about customers as is feasible has become standard operating procedure in many industries. Likewise, collecting operational data including employee performance metrics in support of process improvement and streamlining has become similarly commonplace, as well.
Oftentimes, however, data that is collected from a given source becomes somewhat siloed and is only considered or put to use in the context of the source from which it came. For example, operational data being used exclusively in operational analysis.
Along those lines, many companies have a substantial amount of data available to them at this point many with regard to employee benefits package choices and usage that they have collected over time through various platforms, applications, websites, and benefits providers. With this data, companies typically then refine benefits offerings on an ongoing basis to best meet employee needs and implement benefits strategy as it evolves.
In limiting employee benefits data to employee benefits analyses, however, companies are potentially missing a major opportunity to put that data to work in a number of other ways that can help the company achieve goals well beyond the scope of employee benefits optimization.
Through benefits-related data analysis, companies can get a more complete picture of their employee pool - as a whole and as individuals - including demographic data and benefit utilization, of course. With regard to benefit utilization, if the data reveals an especially popular voluntary benefit among employees, the employer might choose to fund that particular option, for example. Beyond the benefits context, the same data might reveal employee content engagement patterns and preferences that can help shape future intra-company messaging and communication strategies, both about benefits and other topics as well.
Similarly, an analysis of take-up rates with regard to a given benefit can help employers locate employee engagement gaps. If employees have a low-engagement rate for a particular benefit, that benefit is probably not particularly popular with employees and should be addressed by benefits managers as an isolated issue. If the engagement is more widespread across the benefits package and/or employee population, then the problem is likely more foundational and must be remedied through a larger overhaul including improved communication and education for employees about benefit value. Further, understanding the means through which engagement gaps were bridged with a given employee can potentially be useful in bolstering engagement outside of the benefits context, as well.
Analyzing benefits data can also serve as a detection system that can help employers identify employees who are experiencing financial distress and intervene before the situation worsens to the point that it becomes a bigger problem for both the employee and their employer. One of the first signs of an employee having difficulty making ends meet is their opting out of benefits, especially en masse. By setting up alerts for certain benefit-dropping behavior, employers may be able to discover employees who are struggling and offer them support at a time when they need it most.
Ultimately, by utilizing the data that can be mined from employee benefits platforms, employers have an opportunity not only to improve their benefits packages and the offerings within to better meet their objectives on a near continuous basis, they can also put that very same data to work improving other aspects of their business at the same time - which is a lesson about data that applies well beyond benefit-related data, as well.
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