SYSTEM AND METHOD FOR REAL-TIME PREDICTION OF CUSTOMER SATISFACTION

• US 20100332287A1
• Filed: 06/24/2009
• Published: 12/30/2010
• Est. Priority Date: 06/24/2009
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Park, Youngja, Gates, Stephen C.
• Application Number:
  US12/491,095
• Publication Number:
  US 20100332287A1
• Time in Patent Office: Days
• US Class Current: 705/7.32
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0245 Surveys
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements

Abstract

A system and method for real-time prediction of contact center customer satisfaction including means and steps for capturing an interaction between a customer and a customer service agent, converting the captured interaction into transcribed text, analyzing the transcribed text to extract a plurality of unstructured features most closely related to customer satisfaction, combining the extracted features with a plurality of structured features obtained from other contact center data, generating a customer satisfaction score from the combination of extracted unstructured features and structured features, and presenting the customer satisfaction score to contact center personnel.

Claims

1. A method for real-time prediction of customer satisfaction for a contact center interaction between a customer and at least one customer service agent at the contact center comprising steps of:

obtaining an interaction text representing the contents of said contact center interaction;

extracting out of said interaction text a plurality of unstructured features based on a stored set of unstructured features previously identified as most closely related to customer satisfaction;

identifying out of a contact center database a plurality of structured features from among a stored set of structured features previously identified as most closely related to customer satisfaction;

generating a predicted customer satisfaction score from a combination of extracted unstructured and identified structured features; and

presenting the predicted customer satisfaction score.

2. The method of claim 1 wherein said generating a customer satisfaction score comprises steps of:

selecting at least one customer satisfaction model; and

applying the at least one customer satisfaction model to said combination of extracted unstructured and structured features to produce a customer satisfaction score.

3. The method of claim 2 wherein said selecting of at least one customer satisfaction model comprises identifying which of a plurality of customer satisfaction models best predicts observed customer satisfaction scores, said identifying comprising the steps of:

retrieving at least one stored customer satisfaction model;

selecting at least one stored customer interaction;

obtaining a plurality of data sets by computing for each customer interaction a set consisting of a plurality of unstructured and structured features, and a matching set containing at least one previously-obtained customer satisfaction score for each of said at least one stored customer interaction;

computing a plurality of predicted customer satisfaction scores for each of the at least one customer interactions based upon each said set consisting of a plurality of unstructured and structured features and each of said stored customer satisfaction models;

calculating a measure of degree of match between each of said plurality of predicted satisfaction scores and the previously-observed customer satisfaction score for the same customer interaction; and

identifying, from the plurality of said measures of degree of match, the model which produces the highest overall degree of match.

4. The method of claim 2 wherein said customer satisfaction model is obtained by the steps of:

selecting a plurality of customer interactions;

obtaining a plurality of structured and unstructured features by computing for each of said customer interactions;

retrieving for each of said features a customer satisfaction score for the corresponding customer interaction; and

applying any of a plurality of machine learning systems to a plurality of said features and said corresponding customer interactions to create a customer satisfaction model.

5. The method of claim 1 wherein said interaction text is selected from at least one of the following:

transcript of a call between the customer and at least one of a contact center agent and other contact center personnel;

transcript of a call between two contact center personnel;

text of an e-mail between the customer and contact center personnel; and

text of a computer chat between the customer and contact center personnel.

6. The method of claim 1 further comprising creating a plurality of partial interaction texts at repeated intervals during the interaction between the customer and the at least one contact center agent, with each of said partial interaction texts being used to create a separate customer satisfaction prediction score.

7. The method of claim 1 further comprising utilizing said real-time prediction of customer satisfaction to trigger a further action, said further action selected from a list of further actions including:

generating alerts to contact center supervisors that a customer may be dissatisfied;

causing display to the contact center agent of suggestions for improving satisfaction by the customer to whom the agent is speaking;

providing information to the agent of additional options for resolving the customer'"'"'s issue; and

displaying specific suggestions to the agent for improving the customer'"'"'s dissatisfaction based upon observed features in the interaction.

8. The method of claim 1 wherein said unstructured features may include features selected from a list of features extracted from the interaction, comprising at least one of the following:

prosodic features;

lexical features;

contextual features; and

acoustic features.

9. The method of claim 1 wherein said presenting the predicted customer satisfaction score comprises at least one of displaying the predicted customer satisfaction score to the customer service agent, displaying the predicted customer satisfaction score to a contact center representative other than the customer service agent, and delivering the predicted customer satisfaction score via electronic mail.

10. The method of claim 1, wherein said set of unstructured features previously identified as most closely related to customer satisfaction is identified by correlating features from stored previous interaction text with customer satisfaction survey results, by steps of:

acquiring stored interaction text of at least one previous interaction;

obtaining survey results of a customer satisfaction survey for the at least one previous interaction; and

identifying features in said interaction text that are related to customer satisfaction.

11. The method of claim 10 wherein said acquiring interaction text and said obtaining survey results comprise accessing a labeled interaction text that contains both the interaction text and at least one label containing the customer satisfaction survey results for that interaction.

12. The method of claim 10 wherein said identifying features in said interaction text that are related to customer satisfaction comprises steps of:

associating the interaction text with the survey results representing the customer satisfaction for the interaction captured in the interaction text;

extracting candidate interaction text features that are associated with said survey results;

computing a relationship score between each extracted interaction text feature and the associated survey results; and

generating a customer satisfaction model comprising a set of candidate features selected as those extracted interaction text features having highest relationship scores.

13. The method of claim 12 further comprising the steps of:

associating stored structured features from said interaction with said survey results representing the customer satisfaction for the same interaction as the structured data;

extracting one or more candidate structured features that are associated with survey results;

computing a relationship between each extracted candidate structured feature and the associated survey results; and

selecting extracted structured features having a strongest relationship for inclusion in said customer satisfaction model.

14. The method of claim 12 further comprising storing said identified features and a corresponding set of rules or statistics for the identified features, said set of rules or statistics selected by finding the rules or statistics that best separate one satisfaction class from other satisfaction class as a customer satisfaction model.

15. The method of claim 12 wherein said computing a relationship comprises steps of:

selecting at least one machine learning program; and

applying the at least one machine learning program to the extracted unstructured features and the associated survey results; and

outputting a relationship value from the machine learning program for each of said extracted unstructured features.

16. The method of claim 13 wherein said computing a relationship comprises steps of:

selecting at least one machine learning program; and

applying the at least one machine learning program to the extracted unstructured features, the extracted structured features and the associated survey results.

17. The method of claim 3 wherein said computing a plurality of predicted customer satisfaction scores for each of the at least one customer interactions based upon the paired data sets comprises invoking more than one machine learning component and further comprising a step of selecting learning results from one of said machine learning components for the customer satisfaction scores.

18. A system for automatically performing for real-time prediction of customer satisfaction for a contact center interaction between a customer and at least one customer service agent at the contact center comprising:

at least one interaction transcript component for obtaining a transcribed text of the interaction;

a customer satisfaction prediction component having at least one processing component for extracting out of said interaction text a plurality of unstructured features based on a stored set of unstructured features previously identified as most closely related to customer satisfaction, identifying out of a contact center database a plurality of structured features from among a stored set of structured features previously identified as most closely related to customer satisfaction, and generating a predicted customer satisfaction score from a combination of extracted unstructured and identified structured features; and

presentation means for presenting the predicted customer satisfaction score to at least one contact center personnel.

19. The system of claim 18 wherein the at least one customer satisfaction prediction component comprises a selector component for selecting at least one customer satisfaction model and applying the at least one customer satisfaction model to said combination of extracted structured and unstructured features.

20. The system of claim 18 further comprising a customer satisfaction model generation component for creating a customer satisfaction model by combining previous structured and previous unstructured features from previous interactions having associated customer satisfaction survey results and wherein the customer satisfaction model is used for generating the predicted customer satisfaction score from the combination of identified unstructured features and structured features.

21. The system of claim 18 wherein the at least one component for obtaining an interaction transcript comprises:

a capture component for capturing a conversation between a customer and a customer service agent; and

a speech transcription component for converting the captured conversation into transcribed text.

22. The system of claim 18 further comprising at least one database for storing at least one of call recordings, call transcripts, a customer satisfaction model and predetermined text and structured features correlated to customer satisfaction.

23. The system of claim 20 wherein the customer satisfaction model generating component further comprises:

a relationship analysis component for identifying features in said interaction text that are related to customer satisfaction by associating the interaction text with the survey results and with at least one stored structured features and extracting candidate interaction text features and stored structured features that are associated with survey results;

a customer satisfaction score component for computing a customer satisfaction score for each extracted interaction text feature and structured feature; and

a selection component for generating a customer satisfaction model comprising a set of candidate features selected as those extracted interaction text features and structured features having highest customer satisfaction scores.

24. The system of claim 23 further comprising at least one storage location for storing said identified features as a customer satisfaction model.

25. The system of claim 23 wherein said customer satisfaction score component computes a customer satisfaction score by selecting at least one machine learning program and invoking said at least one machine learning program to operate on the extracted interaction text features and further comprising a selection component for selecting learning results from one of said at least one machine learning components for the customer satisfaction scores.