AUTOMATED CUSTOMER INTERACTION QUALITY MONITORING
A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program code, the program code executable by the at least one hardware processor to: receive, by a processor, data associated with an interaction between a customer and an agent in a contact center, automatically analyze, by the processor, said data to identify one or more topics associated with a content of said interaction, automatically associate, by the processor, one or more questions from a dataset of questions with each of said identified topics, automatically construct, by the processor, a questionnaire comprising said associated questions, and output said questionnaire to said customer.
1 . A system comprising:
at least one hardware processor; and
a non-transitory computer-readable storage medium having stored thereon program code, the program code executable by the at least one hardware processor to:
receive, by a processor, data associated with an interaction between a customer and an agent in a contact center,
automatically analyze, by the processor, said data to identify one or more topics associated with a content of said interaction,
automatically associate, by the processor, one or more questions from a dataset of questions with each of said identified topics,
automatically construct, by the processor, a questionnaire comprising said associated questions, and
output said questionnaire to said customer.
2 . The system of claim 1 , wherein the associating of the one or more questions with each of said identified topics comprises, for each topic of the plurality of tracked topics:
(i) computing a question-topic similarity metric between the text of the question and the topic; and
(ii) associating the topic to the question when the question-topic similarity metric exceeds a threshold.
3 . The system of claim 2 , wherein said questions are selected from the group consisting of: yes/no questions, multiple response questions, numerical value questions, and free text questions.
4 . The system of claim 2 , wherein the computing of the question-topic similarity metric between the text of the question and the topic comprises:
(i) computing a plurality of word similarity metrics, each word similarity metric corresponding to a similarity between a word of the text of the question and a most similar word in the topic; and
(ii) summing the plurality of word similarity metrics to compute the question-topic similarity metric.
5 . The system of claim 1 , wherein said interaction is at least one of: a textual interaction and a verbal interaction.
6 . The system of claim 1 , wherein said analyzing comprises at least one analysis selected from the group consisting of: textual detection, speech detection, speech-to-text detection, sentiment detection analysis, and emotion detection.
7 . The system of claim 6 , wherein said analyzing further comprises arranging said identified topics in a hierarchy of topics, and wherein said constructing further comprises arranging said associated questions based, at least in part, on said hierarchy.
8 . A method comprising:
receiving, by a processor, data associated with an interaction between a customer and an agent in a contact center;
automatically analyzing, by the processor, said data to identify one or more topics associated with a content of said interaction;
automatically associating, by the processor, one or more questions from a dataset of questions with each of said identified topics;
automatically constructing, by the processor, a questionnaire comprising said associated questions; and
outputting said questionnaire to said customer.
9 . The method of claim 8 , wherein the associating of the one or more questions with each of said identified topics comprises, for each topic of the plurality of tracked topics:
(i) computing a question-topic similarity metric between the text of the question and the topic; and
(ii) associating the topic to the question when the question-topic similarity metric exceeds a threshold.
10 . The method of claim 9 , wherein said questions are selected from the group consisting of: yes/no questions, multiple response questions, numerical value questions, and free text questions.
11 . The method of claim 9 , wherein the computing of the question-topic similarity metric between the text of the question and the topic comprises:
(i) computing a plurality of word similarity metrics, each word similarity metric corresponding to a similarity between a word of the text of the question and a most similar word in the topic; and
(ii) summing the plurality of word similarity metrics to compute the question-topic similarity metric.
12 . The method claim 8 , wherein said interaction is at least one of: a textual interaction and a verbal interaction.
13 . The method of claim 8 , wherein said analyzing comprises at least one analysis selected from the group consisting of: textual detection, speech detection, speech-to-text detection, sentiment detection analysis, and emotion detection.
14 . The method of claim 13 , wherein said analyzing further comprises arranging said identified topics in a hierarchy of topics, and wherein said constructing further comprises arranging said associated questions based, at least in part, on said hierarchy.
15 . A computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to:
receive, by a processor, data associated with an interaction between a customer and an agent in a contact center;
automatically analyze, by the processor, said data to identify one or more topics associated with a content of said interaction;
automatically associate, by the processor, one or more questions from a dataset of questions with each of said identified topics;
automatically construct, by the processor, a questionnaire comprising said associated questions; and
output said questionnaire to said customer.
16 . The computer program product of claim 15 , wherein the associating of the one or more questions with each of said identified topics comprises, for each topic of the plurality of tracked topics:
(i) computing a question-topic similarity metric between the text of the question and the topic; and
(ii) associating the topic to the question when the question-topic similarity metric exceeds a threshold.
17 . The computer program product of claim 16 , wherein said questions are selected from the group consisting of: yes/no questions, multiple response questions, numerical value questions, and free text questions.
18 . The computer program product of claim 16 , wherein the computing of the question-topic similarity metric between the text of the question and the topic comprises:
(i) computing a plurality of word similarity metrics, each word similarity metric corresponding to a similarity between a word of the text of the question and a most similar word in the topic; and
(ii) summing the plurality of word similarity metrics to compute the question-topic similarity metric.
19 . The computer program product claim 15 , wherein said interaction is at least one of: a textual interaction and a verbal interaction.
20 . The computer program product of claim 15 , wherein said analyzing comprises at least one analysis selected from the group consisting of: textual detection, speech detection, speech-to-text detection, sentiment detection analysis, and emotion detection.
21 . The computer program product of claim 20 , wherein said analyzing further comprises arranging said identified topics in a hierarchy of topics, and wherein said constructing further comprises arranging said associated questions based, at least in part, on said hierarchy.