Turnover prediction & flight-risk models

The core idea

A turnover model estimates a defined departure outcome within a specified future period. It predicts risk, not certainty or the reason a person may leave. Possible inputs require justification: tenure, work patterns and prior experience may be relevant, while absence, pay, location or commute can introduce sensitivity, proxy effects and misleading interpretation. Do not assume that an available field should be used. A useful retention strategy may need only cohort-level analysis and a better process, rather than scores for named employees.

Using it in practice

Define the outcome, prediction date and information available at that date. Use a later holdout period to test generalisation, compare a simple baseline and examine calibration as well as ranking. Check missing data, subgroup errors and changes over time. Decide what supportive, proportionate action could follow a score before building the model. Restrict access and establish a route to question errors. Prediction alone cannot tell you which intervention will change an outcome.

What to watch for

Do not use a risk score to deny training, promotion or fair treatment. Historical data can reproduce past inequities, and removing protected fields does not remove all proxies. A model may predict resignation without identifying an effective retention lever. UK data protection and equality duties require specific assessment; this educational guide does not authorise processing or automate employment decisions.

Prevent leakage

Exclude information that only becomes known after the prediction date, such as a recorded resignation or an exit-interview field. Keep related records together when splitting data. A model that looks excellent because it sees the future will disappoint in actual use.

Evaluate beyond accuracy

If only 10% leave, predicting that nobody leaves is already 90% accurate. Examine precision, recall, calibration and the decision threshold. Check performance across relevant groups with enough data and appropriate governance. Report uncertainty rather than only a single favourable score.

Prediction is not treatment effect

The people most likely to leave are not necessarily those most helped by a particular intervention. Test a supportive action with a suitable evaluation design. Consider whether a universal process improvement would be fairer and more useful than individual targeting.

UK governance: check current rules

The ICO confirmed a major DUAA commencement phase on 5 February 2026, while some older guidance remains marked for review. Do not rely on an old Article 22 summary as a complete current assessment. Establish the applicable lawful basis, transparency, minimisation, retention, security and any additional conditions for special-category data. Assess significant automated decisions and meaningful human involvement against the current law and ICO guidance before implementation.

Related terms