Decision support to customer decrement detection at the early stage for theme parks

In recent years, a theme park drives significant attention in tourism industry due to the provision of quality and integrated service, and issuing annual pass cards help the theme park to differentiate long-term customers from short-term ones. Customer Value Analysis is demanded for theme parks to identify potential customers as well as to appraise customer value through the setting of the annual pass. Moreover, customer value often alters from time to time since theme park industry is relevantly competitive and innovation demanded than other industries, and customer preferences are frequently changed. This study provides an early warning system to support the theme park to detect, monitor and analyze the changes of customer value. By applying the aggregated approach based on Rough Set Theory and Recency, Frequency and Monetary architecture, the tourist satisfaction levels can be captured after the aforementioned approach is executed. In addition, the rule comparison approach is contributed to predicting customer behavior from technical viewpoint. This study aims at providing an early correction strategy for the theme park to avoid losing VIP customers and identify latent customers. •The Customer Value Analysis is demanded for the theme park to identify potential customers in tourism industry.•This study provides an early warning system to analyze the changes of customer value.•The aggregated approach based on Rough Set Theory and REM architectures applied.•Providing an early correction strategy to avoid losing VIP customers.