Organisational behaviour
Employee churn analytics
Employee churn analytics is the process of assessing your staff turnover rates in the past in an attempt to predict the future.
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Employee churn analytics vs Employee performance analytics
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Organisational behaviour
Employee performance analytics
Employee performance analytics seeks to assess individual employee performance.
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Use this when.
Review churn at least every six months or annually, with more frequent monitoring in volatile or high turnover operations. A call centre, for example, may need a different cadence and benchmark from a stable manufacturing site.
Use performance analytics when an annual manager appraisal is too infrequent or too narrow to guide day to day improvement. Regular, proportionate evidence can make coaching more timely and reduce dependence on memory or one person’s impression.
Extracted signals
Strengths, limits, and pitfalls.
Employee churn analytics
- Replace a single annual snapshot with a rolling sample. Invite a 10th of the workforce to respond each month for 10 months. Each person still completes the survey only once, but the organisation receives 10 observation points and can detect change sooner. Design the sample so small teams remain confidential and seasonal effects are visible.
- Translate the findings into changes to work design, management practice, development, pay or scheduling, then monitor whether both churn and employee experience improve. Validate any predictive model for error and unequal impact. Managers should receive actionable group level insight, not opaque risk scores used to disadvantage named employees.
Watch for
- Collecting feedback without acting on it can deepen cynicism.
Employee performance analytics
- Assume that measurement changes behaviour. Use a balanced set of measures, explain why each one matters and review whether employees are shifting effort toward the metric at the expense of customers, colleagues or long term quality. Combine data with a documented conversation and a route to correct errors.
- Start with the job’s real purpose and define a balanced set of measures that employees can understand and influence. Combine outcomes with quality, collaboration and relevant contextual factors. Establish a lawful basis, minimise collection, restrict access and tell employees what data are used and how they can challenge an error.
- Analysis can include Voice Analytics, Text Analytics, Data Mining and Regression Analysis. Each technique should answer a defined question, be validated for the population and support human review. Correlation must not be presented as causation.
Watch for
- More data do not automatically create a fairer assessment.
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