A leading asset management company faced high churn among its field sales teams due to disparate data sources, unstructured HR files, and poor visibility into performance and training data. Aspire Systems created a unified single view of each employee, cleansed and curated datasets, and performed exploratory data analysis to identify churn drivers. Using Python, Pandas, Seaborn, Matplotlib, and Scikit-learn, they built a classification-based prediction model. The solution provided clarity on churn factors, improved prediction accuracy, and delivered a future-proof, upgradeable system to help retain sales talent and strengthen workforce stability.

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