USING MACHINE LEARNING TO PREDICT EARLY SERVICE SEPARATION OF TECHNICAL AND NON-TECHNICAL SAILORS

Sailors are difficult to recruit, expensive to train, and hard to retain. This is particularly true in the technical sailor community. Retention of both technical and non-technical sailors is critical to future manning continuity and capability within the Royal Australian Navy. This research employs machine learning to analyze Royal Australian Navy exit survey data collected between 1999 and 2018 to better predict the attitudes and behaviors of a sailor voluntarily separating between four and eight years of service. Furthermore, this study analyzes in particular whether technical sailors behave differently compared to non-technical sailors. In comparison to traditional modeling techniques, the analysis finds that machine learning can more accurately detect differences in the attitudes and behaviors of technical and non-technical sailors when they are deciding to voluntarily separate from service. Furthermore, the analysis can identify differences in sentiment across periods of time covering key career milestones. This analysis and its findings may now be employed to analyze specific critical target groups in both the Royal Australian Navy technical and non-technical sailor communities to understand their attitudes and behaviors, and help support current and future sailor retention policy initiatives.http://archive.org/details/usingmachinelear1094564123Lieutenant Commander, Royal Australian NavyApproved for public release; distribution is unlimited