Estimating Millions of Dynamic Timing Patterns in Real-Time

In some business applications, the transaction behavior of each customer is tracked separately with a customer signature. A customer’s signature for buying behavior, for example, may contain information on the likely place of purchase, value of goods purchased, type of goods purchased, and timing of purchases. The signature may be updated whenever the customer makes a transaction, and, because of storage limitations, the updating may be able to use only the new transaction and the summarized information in the customer’s current signature. Standard sequential updating schemes, such as exponentially weighted moving averaging, can be used to update a characteristic that is observed at random, but timing variables like day-of-week are not observed at random, and standard sequential estimates of their distributions can be badly biased. This paper derives a fast, space-efficient sequential estimator for timing distributions that is based on a Poisson model that has periodic rates that may evolve over time. The sequential estimator is a variant of an exponentially weighted moving average. It approximates the posterior mean under a dynamic Poisson timing model and has good asymptotic properties. Simulations show that it also has good finite sample properties. A telecommunications application to a random sample of 2,000 customers shows that the model assumptions are adequate and that the sequential estimator can be useful in practice.