Predictive Analytics & ML
Training Recommendation Model Tied to Customer Survey Outcomes
Client: Global automotive manufacturer
Situation
Customer survey outcomes and training data existed in separate systems across the global dealer network, limiting the ability to tie learning programs to customer experience results.
Task
Unify survey outcomes with course completion history and produce actionable recommendations for training that improves specific deficiencies.
Action
Consolidated survey outcomes into a unified dataset spanning sales and service. Joined to global dealer personnel training records. Built a recommendation model in R using classification and regression approaches including Random Forest, producing recommended courses by deficiency pattern. Delivered outputs in Tableau with individual employee drill-down and dealership rollups.
Results
- Enabled targeted training interventions by specific deficiency pattern
- Shifted training from generic pushes to evidence-based recommendations
- Provided dealership managers with actionable employee-level insights
Transformation
Before: One-size-fits-all training
Training programs were assigned generically with no connection to actual customer feedback or performance gaps. Survey data and training records lived in separate systems across a global dealer network.
- Training disconnected from customer feedback
- Survey data and training records in separate systems
- No way to identify specific skill gaps
- Generic training pushes wasting time and budget
After: Targeted, evidence-based training
A recommendation model matches specific customer survey deficiencies to relevant training courses. Managers see employee-level insights with dealership rollups in interactive dashboards.
- Training recommendations tied to specific deficiency patterns
- Unified view of survey outcomes and training history
- Employee-level insights for managers
- Evidence-based training replacing generic pushes
Key Metrics
Training targeting
Data integration
Manager visibility
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