Data-driven talent management predictive modeling for human capital decisions in the BPO industry
Introduction: This article is the result of the research project “Model for Profiling and Personnel Selection Applied to the BPO Sector in Colombia Based on Predictive Analytics Techniques and Genetic Algorithms”, developed at Universidad Distrital Francisco José de Caldas in 2025.
Problem: The organization under analysis exhibited early attrition of 62.82%, resulting in high costs associated with recruitment, training, and the learning curve, which revealed limitations in traditional recruitment processes.
Objective: To design and evaluate a predictive model that identifies candidates with the highest probability of at least 3 months of tenure and determines the most influential attributes for such retention.
Methodology: A mixed approach was adopted. Relevant variables were initially identified through consultation with recruitment professionals; subsequently, machine learning models were trained on 560 hiring records. Algorithms including SVC, Random Forest, and KNN were applied. Class imbalance was addressed using SMOTE, and performance was evaluated using metrics such as accuracy, sensitivity, specificity, and F1-score.
Results: The proposed model reduced early attrition, through simulation, from 62.82% to 4.11%, representing a relative reduction of 93.6%, along with estimated savings of up to COP $1B in one quarter. The SVC model stood out with a specificity of 94.44%, minimizing high-risk hires.
Conclusions: Predictive models in recruitment significantly improve retention and resource optimization.
Originality: The study links predictive accuracy with measurable financial impact in the Colombian BPO sector.
Limitations: Sensitive attributes were not collected to prevent discriminatory bias.
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