Taiwan and many countries are rapidly entering an aging and super-aged society, posing significant challenges to healthcare and elderly welfare systems. In Taiwan, the population aged 65 and above is projected to exceed 20% by 2025. In response, both government and private sectors are promoting health promotion and senior-oriented service models. Lohas Company focuses on senior fitness services with a “one Lohas center in every township” strategy, where effective recruitment of new members is a key operational challenge. This study applies artificial intelligence and data science techniques to analyze member and potential customer data. By integrating demographic information, purchasing behavior, equipment usage frequency, and health improvement indicators, a potential customer classification model is developed using machine learning and neural network methods. The study further explores the relationship between consumption patterns and health promotion outcomes to identify high-value customer segments. The results aim to enhance conversion rates, improve recruitment effectiveness, and provide data-driven recommendations to support precision marketing and strengthen confidence in senior health promotion services.

Keywords

Aging society, Health promotion, Machine learning, Customer classification, Big data analytics