Infrastructure for Employee Turnover Prediction

Turnover prediction requires documented retention policy frameworks—what triggers a retention conversation, what interventions are available (pay adjustment, schedule change, role transition), and what manager authority exists for each intervention. HR policies are documented for labor law compliance per baseline, and performance review processes are defined. However, retention decision-making is informal and workforce planning models are tacit. The model can identify flight risk; without documented intervention protocols, managers receive risk scores without a defined response playbook, limiting the AI's operational value to alerting rather than guided action.

Turnover prediction requires systematic capture of engagement survey results, performance review ratings, schedule adherence patterns, sick leave utilization, and compensation-to-market data. HRIS captures demographics, compensation, and job history systematically per baseline. Time and attendance is automated. Template-driven capture ensures the model receives consistent feature sets across all employees. Without systematic capture of engagement scores and performance ratings in structured fields, the model cannot identify the behavioral and attitudinal signals that precede voluntary resignation.

Turnover prediction ML requires consistent schema: employee identifier, tenure months, department and unit, compensation band, engagement score, performance rating, sick leave frequency, schedule change requests, and prior-year turnover outcome labels. Job titles and compensation bands are structured per baseline. Organizational hierarchy is modeled. Consistent schema enables feature engineering across the employee population. Without standardized performance review criteria and engagement score fields, the model cannot compute comparable risk scores across departments.

Turnover prediction must access HRIS for demographics, compensation, and job history; time and attendance for schedule and absence patterns; engagement survey platforms; and performance management systems. The baseline confirms HRIS has a reporting interface and some API capabilities exist but are underutilized. Multiple HR systems from different vendors create fragmentation. The model accesses data primarily through reporting exports rather than direct API queries, placing accessibility at L2—manual export/import steps remain for some data sources critical to flight risk modeling.

Turnover prediction models require recalibration as labor market conditions change, as compensation benchmarks shift, and as new engagement survey cycles complete. Employee data is updated as changes occur per baseline—promotions and transfers propagate. However, job descriptions are rarely updated, and competency requirements are not reassessed. The model's feature set includes some data elements (market compensation comparisons, engagement scores) that update on irregular cycles rather than continuously. Scheduled periodic recalibration aligned to annual engagement survey cycles is the practical maintenance level achievable.

Employee turnover prediction primarily operates within HR systems—HRIS, performance management, engagement survey platforms, and time and attendance. The baseline confirms HRIS integrates with payroll and time and attendance flows to payroll. However, scheduling is separate, learning management is separate, and there is no integration with clinical quality or patient outcomes. Point-to-point integrations cover the payroll ecosystem but not the full feature set the turnover model requires. The model operates on HR system data without integration to operational systems that could provide early behavioral signals.