Prescriptive analytics are used to “prescribe” actions based on different possible outcomes and to provide guidance toward a solution. Prescriptive analytics answers the question of: “How can we make it happen?” It integrates past data and predictive models with optimization methods and business principles to offer actionable information to direct decision-making in response to questions like, “What should we do?” An example of a prescriptive People Analytics analytics application might be when a company is looking to select new offerings within its employee benefits options. They may need help determining which future benefits will be the most favorably received and highly utilized by employees while still meeting their budget constraints. They could use prescriptive analytics to predict employee usage and the impact of the different offerings and model various estimates of costs to enable decision-making. Prescriptive analytics might also be employed in more employee-facing ways such as the development of customized career or learning recommendations in which employees’ past skills and experience and current career goals can be combined to identify possible future career paths or recommend the best learning and development resources for their goals.

Prescriptive People Analytics Skills

• Decision Trees: Analyzing decision paths to recommend the most favorable actions based on different scenarios. For example, you could use decision trees to classify passive job applicants based on their qualifications and experiences, streamlining the recruitment process and allowing recruiters to contact the right types of potential candidates for critical talent gaps in the organizations.
• Optimization Modeling: Using optimization techniques to find the best solutions to specific problems. This could be used in people analytics to identify the optimal solutions for complex scheduling, resource allocation, and talent management problems. Imagine optimizing employee work schedules to maximize productivity while considering workload distribution and employee preferences, resulting in improved workforce productivity and increased employee satisfaction. Note. you may want to also learn the concepts and techniques involved in “heuristics” when building optimization models. Heuristics is a problem-solving approach that uses practical methods, even if they aren’t fully optimized or logical, to find a “good enough” solution.
• Simulations: Building simulated models allows you to test various strategies and assess their potential impacts. Simulations can be created manually or through computer programming and enable an organization to evaluate the effectiveness of new programs or actions before implementing them.