Data - Driven Insights - Finding the Answer to the Puzzle
Case Study 2 Better Analytics to Develop a More Optimal Variable Pay Allocation Model and to Forecast Drivers for Attrition
In order to institutionalize a performance-based rewards culture, the organization wanted to differentiate the variable compensation pools allocated to the units in accordance with their performance observed across few key parameters of balance scorecard along with understanding what drives attrition at a granular level and leverage employee data to formulate focused retention strategies.
Data Used: The analysis for the tool was done across multiple parameters like business performance, targets and tolerance zones, total available budget, historic attrition information and employee demographics across levels.
Approach for the Intervention
Performance Differentiated: The first step was to normalize the key parameters in the balanced scorecard and to determine the best degree of performance differentiation using statistical measures within the overall variance.
Optimization: Optimized the differentiated bonus pools based on sub business performance, making dynamic changes to employee rating distributions and average bonus pay outs while still reconciling to the overall total.
Tools Development: Developed an automated spreadsheet tool that took in performance input, while keeping bonus fund allocation criteria to be completely flexible and generates the most optimal outcome.
Drivers of Attrition: Formulated key hypothesis and tested them using the data to understand what drove attrition and to identify effective retention factors that have shown significant impact in the past.
Forecasting Model: Created ensemble models optimized for minimizing forecasting error that provided a reliable four-month ahead forecast of expected attrition levels across various skills groups.
The Impact: The actionable recommendations enabled the organization to have a more scientific data backed allocation of budgets, and effectively combat attrition and maximize retention. The forecasting model is enabling the organization to develop a proactive hiring plan to ensure availability of critical skills.
The Full Circle: "When people go to work, they shouldn't have to leave their hearts at home."- Betty Bender Evolving workforce and growing economy call for such a deep dive into the data to identify what motivates your employee and how to better their working environment. Harvard Business Review speaks about the Whole Self Model which reflects not just the employee work self but also his/her relationships, internal self and external self. Traditional ways of annual employee touchpoints may be fast moving towards extinction in this scenario. When we look into the future, big data might be analysing potential candidates peer rated contributions on social media, wearable devices might be sharing insights on employee productivity. The futuristic view may not show a dramatic increase in salary hike. But a 360 degree understanding of employees across the lifecycle may lead the way to a deeper and granular understanding of employees through a combination of data and behavioral science. And not wait to be surprised by the next cycle of attritions and engagement reports.
|