Abstract: A system and a process using that system is provided for creating, analyzing and optimizing a customer journey. The process includes real-time creation and continuing analysis of an “Event Sequence Index,” (ESI) corresponding to a time-stamped labeled set of data points representing cumulative events along the customer journey. The data points are further associated with channels, which are modes of interaction between the customer and the organization, and mapped into a linked directed graph which is amenable to analysis through a recursive pattern matching method, such as a non-deterministic finite automaton, employing DQL (Distributed Query Language). Selected portions of these graphs can be identified, either statistically or causally, as signatures of highly satisfactory or unsatisfactory outcomes and may be stored in memory as real-time predictors of the course of a present customer experience and to suggest statistically feasible and effective interventions.

Abstract: A system and a process using that system is provided for creating, analyzing and optimizing a customer journey. The process includes real-time creation and continuing analysis of an “Event Sequence Index,” (ESI) corresponding to a time-stamped labeled set of data points representing cumulative events along the customer journey. The data points are further associated with channels, which are modes of interaction between the customer and the organization, and mapped into a linked directed graph which is amenable to analysis through a recursive pattern matching method, such as a non-deterministic finite automaton, employing DQL (Distributed Query Language). Selected portions of these graphs can be identified, either statistically or causally, as signatures of highly satisfactory or unsatisfactory outcomes and may be stored in memory as real-time predictors of the course of a present customer experience and to suggest statistically feasible and effective interventions.