US 12,282,878 B2
Method and system for identifying actions to improve customer experience
Dipanjan Das, Frisco, TX (US); Rana Saha, Conshohocken, PA (US); Kumar Priyadarshi, Irving, TX (US); Himanshu Shukla, Charlotte, NC (US); and Prakash Hariharan, Bangalore (IN)
Assigned to Genpact USA, Inc., New York, NY (US)
Filed by Genpact USA, Inc., New York, NY (US)
Filed on Aug. 19, 2021, as Appl. No. 17/406,565.
Prior Publication US 2023/0059500 A1, Feb. 23, 2023
Int. Cl. G06Q 10/0639 (2023.01); G06Q 30/016 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0282 (2023.01)
CPC G06Q 10/06393 (2013.01) [G06Q 30/016 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0282 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for automatically detecting and evaluating experience data associated with an experience journey to identify and adjust an action that is taken to improve customer experience, the method comprising:
collecting data associated with the experience journey from a plurality of network and computer sources via hardware hubs based on user authentication;
processing the collected data to prepare experience data used to train a machine learning model, the experience data including customer feedback and objective interaction data;
training the machine learning model using the experience data;
generating a plurality of performance data associated with the experience journey using the trained machine learning model;
identifying individual performance data from the plurality of performance data when the individual performance data has changed over an individual threshold in a time period;
determining an action for solving a network issue associated with the identified performance data based on analyzing hierarchies of the experience data;
performing the action to solve the network issue, wherein performing the action comprises automatically converting one or more emails and inputting the converted emails into an external computing system to reduce a time lag between an email being sent by a user and an index automatically being created by the external computing system;
accessing respective sources of the plurality of network and computer sources based on respective user roles to automatically detect and collect new experience data from the plurality of network and computer sources that reflect improvement on solving the network issue responsive to the action having been taken;
adding the new experience data to retrain and adjust the machine learning model;
updating the plurality of performance data and a next action based on retraining the machine learning model; and
causing a first user interface to be generated on a user device to display a graph of a first hierarchical metric of the plurality of performance data, wherein the first user interface is configured to display a limited set of data points that can be reached directly from the graph, and each of the data points can be selected to launch a second user interface including one or more second hierarchical metrics of the plurality of performance data for drilling down analysis.