Strategic purchasing behaviour has received growing attention in the field of revenue management. Its occurrence potentially hurts providers' revenue with substantial losses. The need to detect strategic customers and predict their decisions has been highlighted by researchers. Theoretical models rely on assumptions about how customers make decisions and what factors influence them. Conditioned experiments are relatively expensive and not representative of the actual system. By comparison, approaches based on statistics and machine learning from historical data can be relatively cheap, representative of actual conditions, and data-based. However, the widely used approaches pose certain challenges, such as interpretability, overfitting, and stability. These may influence their ability to classify - that is, predict customer types and decisions. We propose a conceptual framework and data linkage for detecting strategic customers and predicting customer decisions. The proposed framework and data linkage were developed based on cancel-rebook behaviour by two customer types, namely strategic and myopic. They were also based on two customer decisions: buy or wait. The evidence showed that the input variables in the framework were good predictors of customer types and decisions. Ultimately, we propose a new approach, namely a hierarchical rule-based inferential modelling and prediction, to integrate statistical analysis, rule-based inference, maximum likelihood prediction, and machine learning in a hierarchical structure. The referential value-based discretisation technique used in this approach can alleviate information loss and distortion as an effect of over-generalisation caused by discretisation. It also captures the structure of the data better than other discretisation technique. We used belief-rule-based inference to analyse the relationship between inputs and outputs. An interdependence index was used to measure the relationship between input variables. The hierarchical structure deals with sparse matrices by decomposing input variables into several groups of evidence. The outputs generated by all groups of evidence are then combined to obtain a final inference. The classifiers, developed based on maximum likelihood evidential reasoning (MAKER) framework and a hierarchical rule-based inferential modelling and prediction, are transparent and interpretable. The classifiers perform better than the majority of alternative classification models for both datasets (customer types and customer decisions). Their performance is similar to that of classification trees.