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Predicting Employee Turnover (Churn Prediction)

GFoundry uses machine learning and auto-adaptive models to estimate the likelihood of employee turnover so managers can act early with targeted retention measures.

Updated this week

Who is this for?

HR, People Analytics, and People Managers with access to Intelligence insights.

What it does (at a glance)

  • Produces a risk score per employee.

How it works (signals & features)

The model analyzes multi-source engagement patterns, such as:

  • Platform activity & engagement: frequency, recency, depth, streaks.

  • Learning & growth: course starts/completions, skill progress.

  • Recognition & social signals: peer recognition, feedback volume/ratio.

  • Objectives & performance context: goal updates, check-ins (where available).

  • Survey & pulse indicators: sentiment, well-being, eNPS trends.

  • Tenure & role metadata: contract type, seniority bands.

Notes: Exact features depend on modules enabled and your data-sharing settings.

Privacy, Security & Fairness

  • Anonymized data only: Signals are de-identified/aggregated before modeling; no employee PII is used by the churn model.

  • Purpose-limited: Data is only used to generate turnover risk scores and explanations - never for other purposes or cross-tenant training.

  • Ephemeral processing: After processing and analysis, the source data used for modeling is deleted; only the resulting scores and audit logs remain per your retention settings.

Limitations

  • Predictive scores are probabilities, not certainties. Always combine with qualitative context.

  • Gaps in data (e.g., modules not in use) reduce model confidence.

  • Sudden external events (market, org changes) may shift patterns temporarily.

FAQ

  • How often are scores refreshed?
    Monthly

  • Can we export insights?
    Yes CSV export is available to authorized roles.

  • Does the model learn from outcomes?
    Yes-when HR marks a departure or retention success, models re-weight over time.

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