Customer journey management
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.
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 and one or more customer journeys;
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 and one or more customer journeys, wherein the customer journey score estimates one of customer effort, customer satisfaction, propensity to register a compliant, or how likely to recommend to a friend;
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.
2. The method of claim 1 , wherein the customer device comprises one of a personal computer, a desktop computer, a mobile computer, a laptop computer, a notebook computer, a terminal, a workstation, a server computer, a Personal Digital Assistant (PDA) device, a tablet computer, and a network device.
3. The method of claim 1 , wherein the other device comprises an interactive voice response (IVR) system.
4. The method of claim 1 , wherein the server comprises one of a customer journey score server, a website host server, a polling server and an intervention server.
5. The method of claim 1 , wherein the variable representing a customer journey score describes one of: a number of channels visited over a period of time; a number of web pages visited; a customer's previous activity; average time spent per web session; cost of contract for mobile phone customer; number of previous calls to a call center; call length; number of call transfers; call silences; number of store visits; store waiting times; store traffic volumes; store format; interaction sentiment data; and customer demographics.
6. 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.
7. The method of claim 1 , wherein the variable representing the customer journey score is a dependent variable.
8. The method of claim 1 , wherein the combination of variables includes objective data relating to the customer.
9. The method of claim 1 , wherein the combination of variables includes survey data relating to the customer.
10. The method of claim 1 , 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.
11. The method of claim 1 , 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.
12. 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.
13. The method of claim 1 , comprising if the variable representing the customer journey score is above the threshold, intervening in the customer journey to change the customer journey itself by sending the device information to modify web content.
14. The method of claim 1 , comprising 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.
15. A system for using a predictive model to manage customer journeys, the system comprising:
a memory; and
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 and one or more customer journeys;
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 and one or more customer journeys, wherein the customer journey score estimates one of customer effort, customer satisfaction, propensity to register a compliant, or how likely to recommend to a friend;
determine 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, send information to be displayed.
16. The system of claim 15 , wherein the customer device comprises one of a personal computer, a desktop computer, a mobile computer, a laptop computer, a notebook computer, a terminal, a workstation, a server computer, a Personal Digital Assistant (PDA) device, a tablet computer, and a network device.
17. The system of claim 15 , wherein the other device comprises an interactive voice response (IVR) system.
18. The system of claim 15 , wherein the server comprises one of a customer journey score server, a website host server, a polling server and an intervention server.
19. The system of claim 15 , wherein the variable representing a customer journey score describes one of: a number of channels visited over a period of time; a number of web pages visited; a customer's previous activity; average time spent per web session; cost of contract for mobile phone customer; number of previous calls to a call center; call length; number of call transfers; call silences; number of store visits; store waiting times; store traffic volumes; store format; interaction sentiment data; and customer demographics.
20. The system of claim 15 , 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.