# US 11,451,668 B2 · Customer journey management > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,451,668 B2 - Document ID: US11451668B2 - Status: Granted - Kind: B2 - Application number: 17/245,413 - Filed: 2021-04-30 - Published: 2021-08-12 - Granted: 2022-09-20 - Earliest claimed: 2015-09-29 - Assignees: Nice Ltd. - Applicants: Nice Ltd. - Inventors: Natalia Beatriz Piaggio; Leonard Michael Newnham - Web page: https://patentgrep.com/patent/US11451668B2 ## CPC - H04M 3/5191: {interacting with the Internet} - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06Q 30/0203: Market surveys; Market polls - H04M 3/5141: {Details of processing calls and other types of contacts in an unified manner (unified messaging in packet-switching networks H04L51/56)} - H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Systems and methods of managing customer journeys are implemented using one or more processors in a computing system. Each journey may comprise a succession of interactions at interaction points such as telephone conversations, responses to an interactive voice response “IVR” system and viewing a web page. Customer journey scores are determined for customers at one or more interaction points along the customer journey and the customer journey score is used to determine whether and when an intervention should take place. Models for determining customer journey scores may be created for a set of customers based on one or both of subjective and objective data relating to a subset of the set of customers that have made some or part of the journey, e.g. customers that have responded to polls. An intervention may take place during the journey or after completion of the journey. ## Claims ### Claim 1 A method of using a predictive model to manage customer journeys, the method comprising using one or more processors in a computer server: receiving data defining a plurality of customer journeys, each customer journey comprising a succession of logged events representing a historical trail of previous actions performed by a particular customer in a computing system, each event corresponding to an interaction between a customer device and a server or other device; retrieving from computer data storage a combination of variables relating to a customer; using a predictive model to determine a variable representing a customer journey score for the customer based on the combination of variables relating to the customer; determining if the variable representing the customer journey score is above a threshold; and if the variable representing the customer journey score is above the threshold, sending information to be displayed. ### Claim 2 (depends on 1) The method of claim 1, wherein each event corresponds to the interaction at an interaction point between the customer device and the server or other device; and the steps of retrieving, using, determining and sending occur for each interaction point of the customer journey. ### Claim 3 (depends on 1) The method of claim 1, wherein the variable representing the customer journey score is a dependent variable. ### Claim 4 (depends on 1) The method of claim 1, wherein the combination of variables includes objective data relating to the customer. ### Claim 5 (depends on 1) The method of claim 1, wherein the combination of variables includes survey data relating to the customer. ### Claim 6 (depends on 5) The method of claim 5, wherein the predictive model is built by executing a continuous loop in which each iteration of the continuous loop comprises: collecting the survey data from a sample of customers; and using machine learning to build the predictive model based on the accumulated survey data. ### Claim 7 (depends on 6) The method of claim 6, wherein the predictive model is built using machine learning to learn that one or more of the variables relating to the customer in the predictive model do not affect the variable representing the customer journey score and ceasing to retrieve the one or more of the variables relating to the customer in future uses of the predictive model to determine the variable representing the customer journey score. ### Claim 8 (depends on 1) The method of claim 1, wherein the predictive model is built by storing, in computer data storage, data structures including one or more location identifiers for one or more predictive models, a set of values for a plurality of independent variables, a set of values for one or more dependent variables, suggested action rules for one or more interventions, and computer-executable instructions for performing the suggested action rules. ### Claim 9 (depends on 1) The method of claim 1, wherein if the variable representing the customer journey score is above the threshold, comprising intervening in the customer journey to change the customer journey itself by sending the device information to modify web content. ### Claim 10 (depends on 1) The method of claim 1, wherein if the variable representing the customer journey score is above the threshold, comprising intervening in the customer journey by sending information to be displayed in a pop-up window on the customer device. ### Claim 11 (depends on 1) The method of claim 1, wherein the information to be displayed to the customer is an email containing a personalized discount or offer. ### Claim 12 (depends on 5) The method of claim 5, wherein the survey data is collected from a sample of customers triggered at predetermined interaction points during the customers' journeys. ### Claim 13 (depends on 5) The method of claim 5, wherein the variable representing the customer journey score is determined for the customer for whom there is no survey data by extrapolating obtained survey data to the customer who was not polled. ### Claim 14 (depends on 1) The method of claim 1, wherein the predictive model is defined at each of a plurality of interaction points based on one or more variables representing survey data obtained from customers within a number of journey points from the interaction point. ### Claim 15 (depends on 1) The method of claim 1 comprising, using said one or more processors, determining the variable representing the customer journey score as a weighted sum of the combination of variables relating to the customer. ### Claim 16 (depends on 1) The method of claim 1 comprising, using said one or more processors: retrieving from computer data storage descriptive data relating to the customer; and using the predictive model to determine the variable representing the customer journey score at each interaction point based on a combination of independent variables representing objective, survey and descriptive data. ### Claim 17 (depends on 1) The method of claim 1, wherein the variable representing the customer journey score is an estimate of customer satisfaction. ### Claim 18 (depends on 1) The method of claim 1, wherein the customer journey comprises interactions occurring over multiple communication channels. ### Claim 19 (depends on 3) The method of claim 3, wherein the survey data is obtained by polling a random sampling of customers at an interaction point along the journey. ### Claim 20 (depends on 1) The method of claim 1, wherein said determining if the variable representing the customer journey score is above a threshold is triggered by an interaction at a predetermined interaction point by the customer in the customer journey. ### Claim 21 (depends on 1) The method of claim 1 comprising, using said one or more processors, retrieving the predictive model from among a set of multiple predictive models for determining multiple respective variables, each of the multiple respective variables being a measure of a different customer journey score. ### Claim 22 (depends on 21) The method of claim 21 comprising, using said one or more processors, selecting the predictive model associated with a particular location of an interaction point in the customer journey. ### Claim 23 (depends on 20) The method of claim 20, wherein the predictive model associated with the particular location is built by filtering customers from the plurality of customers that have visited the particular location and using only data representing previous actions performed by the filtered customers. ### Claim 24 A system for using a predictive model to manage customer journeys, the system comprising: a computer server comprising one or more processors configured to: receive data defining a plurality of customer journeys, each customer journey comprising a succession of logged events representing a historical trail of previous actions performed by a particular customer in a computing system, each event corresponding to an interaction between a customer device and a server or other device, retrieve from computer data storage a combination of variables relating to a customer, use a predictive model to determine a variable representing a customer journey score for the customer based on the combination of variables relating to the customer, determine if the variable representing the customer journey score is above a threshold, and if the variable representing the customer journey score is above a threshold, send information to be displayed. ### Claim 25 (depends on 24) The system of claim 24, wherein each event corresponds to the interaction at an interaction point between the customer device and the server or other device; and the steps of retrieving, using, determining and sending occur for each interaction point of the customer journey. ### Claim 26 (depends on 24) The system of claim 24, wherein the variable representing the customer journey score is a dependent variable. ### Claim 27 (depends on 24) The system of claim 24, wherein the combination of variables includes objective data relating to the customer. ### Claim 28 (depends on 24) The system of claim 24, wherein the combination of variables includes survey data relating to the customer. ### Claim 29 (depends on 26) The system of claim 26, wherein the predictive model is built by executing a continuous loop in which each iteration of the continuous loop comprises: collect the survey data from a sample of customers, and use machine learning to build the predictive model based on the accumulated survey data. ### Claim 30 (depends on 24) The system of claim 24, wherein the predictive model is built using machine learning to learn that one or more of the variables relating to the customer in the predictive model do not affect the variable representing the customer journey score and cease to retrieve the one or more of the variables relating to the customer in future uses of the predictive model to determine the variable representing the customer journey score. ### Claim 31 (depends on 24) The system of claim 24 comprising computer data storage configured to store data structures including one or more location identifiers for one or more predictive models, a set of values for a plurality of independent variables, a set of values for one or more dependent variables, suggested action rules for one or more interventions, and computer-executable instructions for performing the suggested action rules. ### Claim 32 (depends on 24) The system of claim 24, wherein if the variable representing the customer journey score is above the threshold, changing the customer journey itself by sending the device information to modify web content. ### Claim 33 (depends on 24) The system of claim 24, wherein the information to be displayed to the customer is an email containing a personalized discount or offer. ### Claim 34 (depends on 24) The system of claim 24, wherein the one or more processors are configured to: retrieve from computer data storage descriptive data relating to the customer, and use the predictive model to determine the variable representing the customer journey score at each interaction point based on a combination of independent variables representing objective, survey and descriptive data. ### Claim 35 (depends on 24) The system of claim 24, wherein the one or more processors are configured to retrieve the predictive model from among a set of multiple predictive models for determining multiple respective variables, each of the multiple respective variables being a measure of a different customer journey score.