Accurate predictions of a customer’s activity status and future purchase propensities are crucial for managing customer relationships. This article extends the recency–frequency paradigm of customer-base analysis by integrating regularity in interpurchase timing in a modeling framework. By definition, regularity implies less variation in timing patterns and thus better predictability. Whereas most stochastic customer behavior models assume a Poisson process of “random” purchase occurrence, allowing for regularity in the purchase timings is beneficial in noncontractual settings because it improves inferences about customers’ latent activity status. This especially applies to those valuable customers who were previously very frequently active but have recently exhibited a longer purchase hiatus. A newly developed generalization of the well-known Pareto/NBD model accounts for varying degrees of regularity across customers by replacing the NBD component with a mixture of gamma distributions (labeled Pareto/GGG). The authors demonstrate the impact of incorporating regularity on forecasting accuracy using an extensive simulation study and a range of empirical applications. Even for mildly regular timing patterns, it is possible to improve customer-level predictions; the stronger the regularity, the greater the gain. Furthermore, the cost in terms of data requirements is marginal because only one additional summary statistic, in addition to recency and frequency, is needed that captures historical transaction timing.