Predictive Decision Analytics for Membership Retention and Expansion in Martial Arts Organisations - Liang Song; Rabiatul-Adawiah Binti Ahmad Rashid; Yong Li; Bin Wang (Decision Making: Applications in Management and Engineering, 2025)
A martial arts organisation that effectively retains its existing members while simultaneously attracting new ones is more likely to experience sustainable growth. Recent studies indicate that Artificial Intelligence (AI)-driven predictive decision analytics can significantly enhance member retention by evaluating training performance and movement precision. The system assesses martial arts competencies through the application of Kernel Principal Component Analysis (KPCA) for reducing spatial-temporal dimensions of contour features, in conjunction with Time Series Analysis (TSA) and human action recognition (HAR) methodologies. This assessment framework employs a hybrid classification model that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The BiLSTM component interprets temporal sequences, while the CNN performs spatial analysis, facilitating a comprehensive evaluation of training movements. The operational structure comprises two sequential stages: initially, it detects standardised movements from expert demonstrations; subsequently, it evaluates learner performance through practical tests, establishing benchmarks for comparison. By monitoring patterns in skill progression, student dedication, and dropout risks, the system enables the provision of personalised training interventions aimed at improving retention rates. The empirical outcomes demonstrate that the model not only enhances training quality and member engagement but also leverages data insights to inform strategic decisions for expanding membership. Developers of this approach employ AI and predictive analytics to innovate martial arts training processes, thereby reducing short-term enrolments while fostering long-term organisational stability