People analytics uses data and analytics techniques to understand and improve the workforce. It can be used in a variety of ways, including:
- Talent management: Analysing data on employee performance, engagement, and retention can help organisations identify top performers, identify areas for improvement, and develop strategies for retaining and developing talent.
- Workforce planning: Analysing data on workforce demographics, skills, and capabilities can help organisations plan for future workforce needs and identify potential gaps.
- Organisational design: Analysing data on employee roles, responsibilities, and how work is done can help organisations optimise their organisational structure and improve efficiency.
- Diversity and inclusion: Analysing data on diversity and inclusion can help organisations identify areas where they may be falling short and develop strategies to create a more inclusive workplace.
To use people analytics, organisations typically need to collect and analyse data from a variety of sources, including performance evaluations, surveys, and HR systems. It may be helpful to work with a data scientist or other analytics expert to help with this process.
Using people analytics
Implementing people analytics
Executing a strategy
Credit Suisse is a large financial services company in Switzerland that employs over 47,000 people. To reduce how often they hire new employees, they tried to predict which current employees were most likely to leave and when they would leave. Depending on an employee’s rank and experience, it costs a company 30-400% of that person’s salary each time they replace them. Therefore, preventing even some turnover could result in significant savings for Credit Suisse.
The company used an algorithm based on several predictive factors to predict which employees were most likely to leave. These included factors like employee experience, age, and performance in their current role. This model could also accurately predict when a certain employee was at risk of leaving by taking into account the number of sick days they had taken recently and whether or not there had been any changes in their work duties or responsibilities.
As it turned out, this approach worked quite well for Credit Suisse. The algorithm helped the company identify high-risk employees up to six months before they decided to leave. It also predicted with 94% accuracy when these employees would leave, allowing them to take action sooner rather than later. Overall, this resulted in significant cost savings for Credit Suisse since the company could hire new employees less frequently and reduce the time it took to train them.