The Pain Point
A major player in facilities management space had been experiencing ongoing issues with uncontrolled absenteeism. Operating over a scale of more than 2700 employees at various end client sites, the financial impact of employee absenteeism was substantial. At this scale, even an improvement of 0.1% in employee absenteeism can see the revenue & margins improve by a 6 digit number – that in itself is multi-folds the ROI invested to build the case for leveraging HR Analytics, & improving business metrics. (Also see, similar HR Analytics application & case studies here)
According to SHRM’s Total Financial Impact of Employee Absences, the average rate of paid time off as a percentage of total workdays across all of the organizations was 8.1% of total payroll costs, whereas the average rate of paid sick time off was 3.5% (of payroll costs) – this number is important to many organizations in order to plan for and control the costs associated with paid sick time. In addition to this, the total cost of Overtime caused due to absences stood at 5.7% (of payroll costs). Coming to productivity side, the average productivity loss associated with an unplanned absence was the highest (36.6%) and productivity loss related to a planned absence was the lowest (22.6%).
As seen from the above statistics, Utilization, Presenteeism & Unplanned Absenteeism can substantially impact business metrics & profitability.
Given the nature of work, the employees would be operating from the client sites with multiple operational constraints, which meant that it was a huge challenge for the HR Team to capture real time data on employee absenteeism & utilization. Hence, to build insights on absenteeism & profiles at high risks, the HR team had to rely on aggregated payroll data (collected post 18th – 20th day of the month to initiate payroll processing) and individual evaluations to determine the preventive cause of action. By design, the unintentional complacency to leverage HR Analytics on historical data was driving in sub-optimal results, off-setting the value unlocked projected by the HR team.
Deep partnering with the Client, we improvised the data strategy, with higher focus on leveraging predictive HR Analytics. This was mainly centered around two key areas:
1. Leveraging Payroll data: The payroll data, being the most accurate & validated dataset, comprised of micro trends attributing to the working hours, salary level, employee absenteeism rate, and reason for absenteeism for each individual employee.
2. Employee-specific data: including personal data such as demographics, marital status, number of different clients, tenure, department, performance level, travel time, supervisor’s age and wages for each individual. The use of these enriched and highly specific datasets helped the team to experiment constructively with various complex forecasting models and statistical methods.
Leveraging these 2 critical datasets, the team discovered critical insights mostly related to employee absenteeism patterns, the Bradford factor, the number of absenteeism days, factors leading to unplanned absenteeism, factors leading to turnover risks, & the impact on business metrics basis performance, namely, Absenteeism Costs (Planned & Unplanned), Turnover Costs, Forced Turnover Costs etc. These insights were previously overlooked but with deliberate focus on HR Analytics tools & techniques, these would enable the HR team build & project high value to business outcomes.
From these critical insights, the team deployed a powerful predictive model capable of automatically collecting and processing large datasets to provide a monthly list of employees at high-risk for absenteeism at department level, the employee absenteeism risk map as per Client sites, & the employee absenteeism triggers (at department & client site level). In addition to this, we extended the analysis to turnover risks as well, building predictive algorithms for high-risk of turnover at department level, turnover risk map as per client sites & turnover triggers.
Post formulation & deployment, to validate its usability, we trended the weekly insights with client’s own internally generated lists, and found the new approach & algorithm were holding up high statistical significance, outperforming the earlier approach, and enabling the HR team to identify & highlight larger relevant pool of employees at high risk of employee absenteeism & turnover.
With targeted and actionable insights, the client’s HR department is now proactively able to take effective preventative measures to reduce employee absenteeism, rather than reactively limited to witnessing the business value erosion in tackling this common resource-intensive problem.