Top-tier customers - that is, those 20% of customers that typically bring in 80% of all profits - are extremely valuable to companies. In the many instances in which organizations attribute top-tier status to customers based on their consumption behaviour within a specific period, such as a year, it becomes very important to determine, during this period, how likely those gold customers are to retain their top-tier status going into the next period. This allows better planning at the corporate level, but can also allow for corrective measures or special retention efforts to be deployed. However, while models exist to predict customer churn or customer lifetime value either at the beginning of a period or on a continuous basis according to the evolution of inter-purchase time, no model allows for a continuous re-estimation of customer status or value according to calendar time, based on historical data and year-to-date information. To address this problem, we develop a model of intra-periodic forecasting of customer behaviour that uses nonhomogeneous Poisson processes with random effects. We then empirically assess the performance of this model using data from the loyalty program at a major commercial airline.