Lessons in Implementing Real-Time Engagement Analytics
Engagement analytics combines predictive analysis with machine learning to deliver actionable management insights, while avoiding survey fatigue by gathering only the most essential feedback from employees.
As a real-time, ongoing process, this element of people analytics renders the notoriously ineffectual annual engagement survey obsolete.
In this workshop we’ll outline the steps organisations should take to successfully implement engagement analytics – based on the experiences of businesses using such technology today. We’ll cover:
- The psychology and data-science of understanding engagement. Identifying 14 drivers of engagement from decades of organisational research and building an algorithm to find which drivers have the greatest effect on engagement in your organisation.
- Creating an unobtrusive feedback mechanism. Meeting engagement challenges today, requires a constant stream of both quantitive and qualitative data from employees. Naturally many fear survey fatigue when moving from an annual or quarterly model. Using machine learning in survey design and data imputation ensures minimal disruption to employees.
- Delivering insights that matter. Providing all levels of management with relevant, well visualised, data enables everyone to play their part, in a coordinated effort to improve engagement. Predictive analysis can show which management initiatives will boost engagement across the company and in every individual team.
- Using engagement scores as management KPIs. As the best indicator of an organisation’s future success, managers should be evaluated on their ability to engage employees. To further strengthen the circle of feedback, actions, and improvements, we’ll look at practical examples of how organisations use engagement data as management performance indicators.
Analytics maturity: Early – Medium