Developing a robust predictive workforce planning model

Predictive retention analytics and productivity modelling

Monitor Deloitte’s Actuarial and Analytical Solutions Practice assists public and private sector institutions to develop strategic workforce plans that help ensure that the right skills are in the right place, at the right time and cost - now and in the future.

Developing a robust predictive workforce planning model

Given the importance of talent and people, it is time to move beyond instinct, gut and tribal wisdom in making workforce decisions. If you are not using workforce data and analytics to drive your talent decisions, you may be behind the curve - and at risk of losing your competitive edge. As HR works with leaders on the front line, analytics are becoming critical in making more effective decisions related to workforce planning and recruitment, risk management, compensation, development programs, and deploying critical talent.

Workforce analytics involves using statistical models that integrate internal and external data to predict future workforce and talent-related behaviour and events. These models can help organisations in any industry focus limited resources on critical talent decisions. For example, models have been demonstrated to predict the likelihood (with a reasonable degree of certainty) that a particular employee will leave in the next six months - and can provide the likely reasons for the predicted exit.

What's driving this trend?

  • The need for foresight: moving from reactive to proactive
  • Falling technology and data costs: new models and tools
  • Data savvy leaders
  • Increased regulation and scrutiny
  • Richer and deeper data

In terms of primary capabilities, the building blocks of a successful workforce analytics and planning program rest on the following general questions:

  •  People: What kind of organisation and specific skills are needed to support an analytics capability?
  • Process: What is the leading way to improve the impact of decision support tools?
  • Technology: What tools and systems are necessary for data-driven decisions?
  • Data: How do we get the most value out of internal and external data?
  • Governance: How will data guide decisions - and who is accountable for implementing them