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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 over a month ago

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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