Predictive Decision Analytics for Membership Retention and Expansion in Martial Arts Organisations

Liang Song; Rabiatul-Adawiah Binti Ahmad Rashid; Yong Li; Bin Wang

doi:10.31181/dmame8220251474

Authors

Liang Song Assistant Professor, Sports & Health college, Sanming University, Sanming, Fujian, China & School of Educational Studies, Universiti Sains Malaysia, Penang, Malaysia
Rabiatul-Adawiah Binti Ahmad Rashid School of Educational Studies, Universiti Sains Malaysia, Penang, Malaysia
Yong Li PHD student, Physical Education College, Henan Institute of Science and Technology, Xingxiang, Henan, China
Bin Wang Assistant Professor, Physical Education Department, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

DOI:

https://doi.org/10.31181/dmame8220251474

Keywords:

Predictive Decision Analytics, Martial Arts Training, Membership Retention, Convolutional Neural Network (CNN), Time Series Analysis (TSA).

Abstract

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

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