Method and apparatus for detection and analysis of first contact resolution failures
First Claim
1. A method for detecting first contact resolution failures, comprising:
- using a processor, converting audio interactions into textual transcripts and textual interactions or receiving textual transcripts from an e-mail server, chat server or social media server,storing in a training corpus the converted or received textual transcripts as pairs of interaction digital representations, each pair is tagged as First Contact Resolution Failure (FCRF) or First Contact Resolution (FCR) interactions pair;
generating and storing in a storage device a topic model including a plurality of weight scores associated with corresponding key phrases, by detecting topics and corresponding sets of phrases in the interaction digital representations;
generating and storing in the storage device a First Contact Resolution Failures (FCRF) model by calculating based on the stored topic model a topic similarity score for each of the stored tagged pairs of interactions, and by executing a training module with the topic similarity score and parameters of each of the stored tagged pairs of interactions as input;
obtaining by a capturing apparatus a first plurality of digital representations, each digital representation of the first plurality is associated with a contact center interaction;
from the first plurality of digital representations, determining that a first and a second of the plurality of digital representations is a pair of digital representations that represent a pair of contact center interactions according to predefined rules applied on metadata associated with the pair of the contact center interactions, and associating the first and second interactions as candidates for contact resolution classification;
applying the stored FCRF model for classification of the pair of contact center interactions candidates;
calculating an FCRF classification score representing the probability that a customer issue was not resolved within the first interaction of the pair of interactions, and handling of the same customer issue continued in the second interaction of the pair of interactions;
comparing the FCRF classification score to a predefined threshold, thereby classifying the pair of contact center interactions candidates as a first contact resolution failure (FCRF) interaction pair if the FCRF classification score is higher than the predefined threshold or classifying the pair of contact center interactions candidates as a first contact resolution (FCR) interactions pair if the FCRF classification score is lower than the predefined threshold; and
generating a report including a list of pairs of interactions that were classified as FCRF pairs of interactions;
wherein the classification of the determined pair of contact center interactions comprises;
determining based on the stored topic model a topic vector for each digital representation of the determined pair of digital representations; and
determining a topic similarity score between the topic vectors associated with the interactions of the determined pair of digital representations.
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Abstract
The subject matter discloses a method for detection and analysis of first contact resolution failures comprising: obtaining a first plurality of digital representations, each digital representation of the first plurality is associated with a contact center interaction; determining a pair of digital representations that represent a pair of contact center interactions determined from the first plurality of digital representations according to metadata associated with the pair of contact center interactions; determining topics of each interaction of the determined pair of contact center interactions represented by the pair of digital representations; classifying the pair of contact center interactions as first contact resolution failure or first contact resolution interactions pair.
25 Citations
21 Claims
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1. A method for detecting first contact resolution failures, comprising:
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using a processor, converting audio interactions into textual transcripts and textual interactions or receiving textual transcripts from an e-mail server, chat server or social media server, storing in a training corpus the converted or received textual transcripts as pairs of interaction digital representations, each pair is tagged as First Contact Resolution Failure (FCRF) or First Contact Resolution (FCR) interactions pair; generating and storing in a storage device a topic model including a plurality of weight scores associated with corresponding key phrases, by detecting topics and corresponding sets of phrases in the interaction digital representations; generating and storing in the storage device a First Contact Resolution Failures (FCRF) model by calculating based on the stored topic model a topic similarity score for each of the stored tagged pairs of interactions, and by executing a training module with the topic similarity score and parameters of each of the stored tagged pairs of interactions as input; obtaining by a capturing apparatus a first plurality of digital representations, each digital representation of the first plurality is associated with a contact center interaction; from the first plurality of digital representations, determining that a first and a second of the plurality of digital representations is a pair of digital representations that represent a pair of contact center interactions according to predefined rules applied on metadata associated with the pair of the contact center interactions, and associating the first and second interactions as candidates for contact resolution classification; applying the stored FCRF model for classification of the pair of contact center interactions candidates; calculating an FCRF classification score representing the probability that a customer issue was not resolved within the first interaction of the pair of interactions, and handling of the same customer issue continued in the second interaction of the pair of interactions; comparing the FCRF classification score to a predefined threshold, thereby classifying the pair of contact center interactions candidates as a first contact resolution failure (FCRF) interaction pair if the FCRF classification score is higher than the predefined threshold or classifying the pair of contact center interactions candidates as a first contact resolution (FCR) interactions pair if the FCRF classification score is lower than the predefined threshold; and generating a report including a list of pairs of interactions that were classified as FCRF pairs of interactions; wherein the classification of the determined pair of contact center interactions comprises; determining based on the stored topic model a topic vector for each digital representation of the determined pair of digital representations; and
determining a topic similarity score between the topic vectors associated with the interactions of the determined pair of digital representations. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 19, 20, 21)
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14. A system for detecting first contact resolution failures, comprising:
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a storage device configured to store a training corpus of pairs of interaction digital representations, at least one hardware processor configured to execute code, the code comprising instructions for; converting audio interactions into textual transcripts and textual interactions or receiving textual transcripts from an e-mail server, chat server or social media server; storing in the training corpus the converted or received textual transcripts as pairs of interaction digital representations, wherein each pair is tagged as First Contact Resolution Failure (FCRF) or First Contact Resolution (FCR) interactions pair; generating and storing in the storage device a topic model including a plurality of weight scores associated with corresponding key phrases, by detecting topics and corresponding sets of phrases in the interaction digital representations; generating and storing in the storage device a First Contact Resolution Failures (FCRF) model by calculating based on the stored topic model a topic similarity score for each of the stored tagged pairs of interactions, and by executing a training module with the topic similarity score and parameters of each of the stored tagged pairs of interactions as input; obtaining by a capturing apparatus a first plurality of digital representations, each digital representation of the first plurality is associated with a contact center interaction; from the first plurality of digital representations, determining that a first and a second of the plurality of digital representations is a pair of digital representations that represent a pair of contact center interactions according to predefined rules applied on metadata associated with the pair of the contact center interactions, and associating the first and second interactions as candidates for contact resolution classification; and applying the stored FCRF model for classification of the pair of contact center interactions candidates; calculating an FCRF classification score representing the probability that a customer issue was not resolved within the first interaction of the pair of interactions, and handling of the same customer issue continued in the second interaction of the pair of interactions; comparing the FCRF classification score to a predefined threshold, thereby classifying the pair of contact center interactions candidates as a first contact resolution failure (FCRF) interaction pair if the FCRF classification score is higher than the predefined threshold or classifying the pair of contact center interactions candidates as a first contact resolution (FCR) interactions pair if the FCRF classification score is lower than the predefined threshold; and generating a report including a list of pairs of interactions that were classified as FCRF pairs of interactions; wherein the classification of the determined pair of contact center interactions comprises;
determining based on the stored topic model a topic vector for each digital representation of the determined pair of digital representations; and
determining a topic similarity score between the topic vectors associated with the interactions of the determined pair of digital representations. - View Dependent Claims (15, 16, 17, 18)
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Specification