Solution Cross-industry
Workforce Analytics and Attrition Prediction
Predict which teams and roles are at risk of losing people, and give managers explainable drivers they can actually act on before a resignation lands.
PredictedRisk ahead of resignation, not after
ExplainableDrivers managers can act on
QuantifiedCost of attrition versus intervention
Typical outcomes for this kind of engagement. Results depend on your data and starting point.
THE CHALLENGE
Organizations learn about attrition when the resignation arrives, by which point the decision has been made and the cost (replacement, ramp time and lost institutional knowledge) is already committed.
- Attrition discovered at resignation, when the decision has already been made
- Exit interviews collecting reasons late and from people with little incentive to be candid
- Turnover reported historically without any forward view of where risk sits
OUR APPROACH
CloudGate builds attrition risk models on Azure Machine Learning using tenure, role, compensation position, internal mobility, span of control and workload patterns, scored at team and role level rather than as individual predictions.
- Attrition risk modeled at team and role level with explainable contributing drivers
- Features covering tenure, compensation position, internal mobility and span of control
- Aggregation thresholds enforced so individual-level inference is not possible
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