METHOD AND APPARATUS FOR DETECTION AND ANALYSIS OF FIRST CONTACT RESOLUTION FAILURES

• US 20140129299A1
• Filed: 11/06/2012
• Published: 05/08/2014
• Est. Priority Date: 11/06/2012
• Status: Active Grant

• Current Assignee: Nice Ltd
• Original Assignee: Nice Systems Limited (Nice Ltd)
• Inventors: Hurvitz, Eyal, Daya, Ezra
• Granted Patent: US 10,242,330 B2
• Time in Patent Office: Days
• US Class Current: 705/7.41
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0633 Workflow analysis
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 10/06395 Quality analysis or management

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.

Claims

1. A method for detecting 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.

2. The method according to claim 1, wherein each digital representation of the first plurality represents an audio interaction or a textual interaction.

3. The method according to claim 1, wherein classifying the determined pair of contact center interactions comprises:

determining 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.

4. The method according to claim 3, wherein classifying the determined pair of contact center interactions comprises:

determining a topic vector for each digital representation of the determined pair of digital representations; and

determining a mutual topic vector.

5. The method according to claim 1, wherein classifying the determined pair of contact center interactions comprises:

obtaining external data associated with each digital representation of the determined pair of digital representations.

6. The method according to claim 1, further comprises:

grouping classified pairs of interactions of contact center interactions to sequences of interactions; and

determining an FCR score for each interaction sequence.

7. The method according to claim 6, further comprises:

storing the FCR scores that are associated with the sequences of interactions.

8. The method according to claim 1, further comprises:

storing the classification of the pair of contact center interactions.

9. The method according to claim 1, further comprises:

enabling a search of the classified pairs of interactions based on the FCRF classification score.

10. The method according to claim 1, further comprises enabling a search of the sequences of interactions based on sequences of interactions FCR scores.

11. The method according to claim 1, further comprises:

generating a report based on pairs of interactions and their associated FCRF classification scores.

12. The method according to claim 1, further comprises:

generating a report based on sequences of interactions and FCR scores of said sequences of interactions.

13. The method according to claim 1, further comprises obtaining a second plurality of digital representations each digital representation is associated with a contact center interaction;

determining a topic model for the second plurality of digital representations;

determining a pair of digital representations that represent a pair of contact center interactions determined from the second plurality of digital representations according to metadata that is associated with the interactions;

tagging each pair of digital representations;

determining topic vectors for each digital representation of the pairs of digital representations;

determining topic similarity scores between each two topic vectors associated with the pairs of digital representations;

determining mutual topic vectors for each digital representation of the pairs of digital representations; and

generating an FCRF model based on the topic similarity scores and/or the mutual topic vectors and or obtained external data that is associated with the interactions.

14. The method according to claim 13, wherein tagging of each pair of digital representations is based on pattern-based information extraction.