Abstract: Disclosed is a method for maintaining compact descriptions of the behavior of customers in a database of information relating to customer transactions. Of all possible variables that might be used to characterize customer transactions, a limited number are selected on the basis of discriminating power to define customer profiles. Customers are classified according to distinguishing features of their profiles. A new customer can be initially assigned to a particular class based on data characterizing his first few transactions.

Abstract: Disclosed is a method for maintaining compact descriptions of the behavior of customers in a database of information relating to customer transactions. Of all possible variables that might be used to characterize customer transactions, a limited number are selected on the basis of discriminating power to define customer profiles. Customers are classified according to distinguishing features of their profiles. A new customer can be initially assigned to a particular class based on data characterizing his first few transactions.

Abstract: Disclosed is a method for maintaining compact descriptions of the behavior of customers in a database of information relating to customer transactions. Of all possible variables that might be used to characterize customer transactions, a limited number are selected on the basis of discriminating power to define customer profiles. Customers are classified according to distinguishing features of their profiles. A new customer can be initially assigned to a particular class based on data characterizing his first few transactions.

Abstract: Disclosed is a method for maintaining compact descriptions of the behavior of customers in a database of information relating to customer transactions. Of all possible variables that might be used to characterize customer transactions, a limited number are selected on the basis of discriminating power to define customer profiles. Customers are classified according to distinguishing features of their profiles. A new customer can be initially assigned to a particular class based on data characterizing his first few transactions.

Abstract: A method is provided for maintaining compact descriptions of the behavior of customers in a database of information relating to customer transactions. Of all possible variables that might be used to characterize customer transactions, a limited number are selected on the basis of discriminating power to define customer profiles. Customers are classified according to distinguishing features of their profiles. A new customer can be initially assigned to a particular class based on data characterizing his first few transactions.

Abstract: A customer signature or other type of record in a database system is updated using an event-driven estimator based on a model of time-dependent behavior. In a preferred embodiment, a current version of a record affected by a given transaction is retrieved from a memory of the system and an event-driven estimator of at least one of a transaction rate and a period probability for the record is determined based at least in part on a dynamic Poisson timing model having a number of periods and corresponding period-based transaction rates associated therewith. The event-driven estimator may be configured so as to generate an estimated transaction rate {circumflex over (&lgr;)}j,n for period j and a given transaction n, and then to generate an estimated period probability {circumflex over (&pgr;)}j,n for period j as {circumflex over (&lgr;)}j,n/&Sgr;k{circumflex over (&lgr;)}k,n. An updated version of the record may then be generated based on the event-driven estimator.

Abstract: A customer signature or other type of record in a database system is updated using an event-driven estimator based on a model of time-dependent behavior. In a preferred embodiment, a current version of a record affected by a given transaction is retrieved from a memory of the system and an event-driven estimator of at least one of a transaction rate and a period probability for the record is determined based at least in part on a dynamic Poisson timing model having a number of periods and corresponding period-based transaction rates associated therewith. The event-driven estimator may be configured so as to generate an estimated transaction rate {circumflex over (&lgr;)}j,n for period j and a given transaction n, and then to generate an estimated period probability {circumflex over (&pgr;)}j,n for period j as {circumflex over (&lgr;)}j,n/&Sgr;k{circumflex over (&lgr;)}k,n. An updated version of the record may then be generated based on the event-driven estimator.