Predictive analytics have been around for a few years now, but 2015 seems to be the year when companies are finally going to begin using predictive analytics to improve recruitment, scheduling, absence issues, and hopefully revenues. Many companies are excited about the prospects of using predictive analytics to change the future, but may not know where to begin. If you find yourself in this boat, consider the following tips to begin using your HRIS more regularly and practically.
Pretty much every company keeps track of absences, logging doctor’s notes and citing reasons in some sort of organized fashion. Unfortunately, most places use view absences with a specific “What is going on with this employee?” approach as opposed to a more general “Should we expect more absences based on X,Y, Z circumstances that affected these employees?” Using HRIS to cross reference employee job titles, demographics, and other information may help managers to anticipate and plan ahead for absences.
One example is a possible absence pattern among parents of children during influenza season. While it may have been easy to determine that employee absences were increased due to a spread of influenza, noting that it is mainly parents that are missing work may help managers to plan ahead and anticipate specific absence risks in advance.
Turnover is an issue that can have serious consequences for organizations when rates are high. Having an idea of what the average turnover rate will be by department and job title can help management to get ahead of the turnover dilemma by getting a jump on recruitment so that there is less downtime in between employee departure and replacement. Predictive analytics may also help managers spot specific patterns in employee departures that can help turn the trend around or at least predict which employees are likely to quit.
While companies are beginning to realize the importance of employee contentment, many organizations simply take surveys to determine the levels of contentment and take steps to increase contentment. While this is not a bad thing, information about employee contentment can be used for so much more!
Employee contentment information can be logged and analyzed so that HRIS recognizes certain keywords, and then the information can be cross referenced against customer satisfaction information, productivity, employee sick leave, training, performance, and a wealth of other data that is relevant to revenues. By understanding how employee contentment affects different areas of the job, managers can develop systems and programs that may directly improve revenues and growth.
In order to use predictive analytics to the furthest means possible, the HRIS must have the ability to not only mine through HR data, but it must be able to pull data from external sources (such as revenues and productivity information) and make comparisons to spot patterns. If a system does not have these capabilities, it will not be possible to use predictive analytics to change the future. At present, this may not seem like a big deal, but it may just affect competitiveness and profitability down the road.
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