Analyzing conversations to automatically identify deals at risk

• US 10,133,999 B2
• Filed: 12/08/2017
• Issued: 11/20/2018
• Est. Priority Date: 06/01/2015
• Status: Active Grant

• Current Assignee: Zoominfo Converse LLC
• Original Assignee: AffectLayer, Inc. (ZoomInfo Technologies, Inc.)
• Inventors: Raanani, Roy, Levy, Russell, Facher, Dominik, Breakstone, Micha Yochanan
• Primary Examiner(s): Addy, Thjuan K
• Application Number:
  US15/835,807
• Publication Number:
  US 20180096271A1
• Time in Patent Office: 347 Days
• Field of Search: 37926507, 37926505, 37926506, 3792661, 37926503
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 17/02 Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
• CPC Class Codes: G10L 17/26 Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
• CPC Class Codes: G10L 2015/088 Word spotting
• CPC Class Codes: G10L 25/51 for comparison or discrimination
• CPC Class Codes: G10L 25/63 for estimating an emotional state
• CPC Class Codes: H04M 2201/40 using speech recognition speech recognition per se G10L15/00
• CPC Class Codes: H04M 2203/357 Autocues for dialog assistance
• CPC Class Codes: H04M 2203/556 Statistical analysis and interpretation
• CPC Class Codes: H04M 3/42221 Conversation recording systems at the subscriber's set H04M1/656
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements
• CPC Class Codes: H04M 3/5232 Call distribution algorithms

Abstract

A risk identification system automatically determines deals at risk by analyzing conversations of representatives with customers. The risk identification system retrieves recordings of various conversations, extracts features of each of the conversations, and analyzes the features to determine if any of the conversations includes features that are indicative of a deal discussed in that conversation being at risk. By performing such an analysis of conversations, the risk identification system can identify a number of deals that are at risk and generate a report of such deals and notify a consumer user of the risk identification system of such deals.

Claims

1. A computer-implemented method, comprising:

retrieving multiple recordings of conversations associated with multiple representatives, wherein each of the conversations is between at least one of the representatives and at least one of multiple customers, and wherein each conversation is associated with a deal offered by the at least one of the representatives to the at least one of the customers;

extracting multiple features from each of the multiple recordings, wherein the multiple features indicate characteristics of any of (a) a customer of multiple customers in the corresponding conversation, (b) a representative of multiple representatives in the corresponding conversation, (c) the corresponding conversation;

analyzing features associated with a first conversation of the conversations to determine if the first conversation includes any feature that is indicative of a first deal associated with the first conversation being at a risk, wherein the analyzing is performed for each of the conversations; and

generating, based on the analyzing, information regarding a set of deals that is at risk, wherein each deal of the set of deals is determined to be at risk in an event one or more conversations with which the corresponding deal is associated includes features that are indicative of the corresponding deal being at risk, the generating further including generating a reason why each deal of the set of deals is at risk.

2. The computer-implemented method of claim 1, wherein

analyzing the features to determine if the first deal is at risk includes:

determining that the first conversation includes a set of features that satisfies a risk criterion.

3. The computer-implemented method of claim 2, wherein the set of features satisfies the risk criterion in an event the set of features is indicative of the first deal being lost.

4. The computer-implemented method of claim 2, wherein the set of features satisfies the risk criterion in an event the set of features is indicative of a reduction in budget associated with the first deal.

5. The computer-implemented method of claim 2, wherein the set of features satisfies the risk criterion in an event the set of features is indicative of a delay in closing of the first deal.

6. The computer-implemented method of claim 2, wherein the set of features satisfies the risk criterion in an event the set of features is indicative of the first deal being not a priority to a customer associated with the first conversation.

7. The computer-implemented method of claim 1, wherein analyzing the features to determine if the first deal is at risk includes determining, based on a usage of a specified word or a phrase by a customer associated with the first conversation, a set of features that is indicative of the first deal being at risk.

8. The computer-implemented method of claim 7, wherein determining the set of features includes determining a first feature from the set of features that is indicative of the customer citing lack of budget for closing the first deal.

9. The computer-implemented method of claim 7, wherein determining the set of features includes determining a first feature from the set of features that is indicative of the customer citing lack of authority for closing the first deal.

10. The computer-implemented method of claim 7, wherein determining the set of features includes determining a first feature from the set of features that is indicative of the customer citing lack of time to review the first deal.

11. The computer-implemented method of claim 7, wherein determining the set of features includes determining a first feature from the set of features that is indicative of the customer citing the first deal as not a priority to the customer.

12. The computer-implemented method of claim 7, wherein determining the set of features includes determining a first feature from the set of features that is indicative of the customer citing timeline of closing the first deal as a problem.

13. The computer-implemented method of claim 7, wherein generating the information regarding the set of deals includes generating the reason based on the set of features using which the first deal is determined to be at risk.

14. The computer-implemented method of claim 13, wherein generating the reason based on the set of features includes generating the reason as at least one of lack of budget for closing the first deal, lack of authority for closing the first deal, lack of time to review the first deal, the first deal not being a priority to the customer, or a timeline for closing the first deal.

15. The computer-implemented method of claim 1, wherein extracting the features includes:

generating features that include a transcription, vocabulary and a language model of the conversations as a first output.

16. The computer-implemented method of claim 15, wherein extracting the features includes:

generating, using the first output, features that include semantic information from the conversations.

17. The computer-implemented method of claim 1, wherein extracting the features includes:

generating a set of low-level features that indicate information associated with a voice signal in the multiple recordings, and a set of high-level features that include personality traits and personal attributes of the multiple representatives and emotion data that indicates emotions of the multiple representatives.

18. The computer-implemented method of claim 1, wherein extracting the features includes generating features that include data regarding conversation flow.

19. The computer-implemented method of claim 1, wherein extracting the features includes generating features related to a representative-customer pair in a conversation of the conversations.

20. The computer-implemented method of claim 1, wherein extracting the features includes:

generating a speaker engagement metric that includes information regarding a degree of engagement of a specified customer of the multiple customers in a conversation of the conversations.

21. The computer-implemented method of claim 1, wherein extracting the first set of features includes extracting a visual feature associated with a conversation of the conversations.

22. The computer-implemented method of claim 1, wherein extracting the features includes extracting the features using any of an artificial intelligence, a machine learning, or natural language processing technique.

23. The computer-implemented method of claim 1, wherein analyzing the features of each of the conversations includes analyzing the features using any of an artificial intelligence, a machine learning, or natural language processing technique.

24. The computer-implemented method of claim 1, wherein at least one of the multiple recordings includes a recording of a video call between one of the customers and one of the representatives.

25. The computer-implemented method of claim 1, wherein at least one of the multiple recordings includes an online meeting between one of the customers and one of the representatives.

26. The computer-implemented method of claim 1, wherein at least one of the multiple recordings includes a recording of a virtual reality-based conversation between one of the customers and one of the representatives.

27. The computer-implemented method of claim 1, wherein at least one of the multiple recordings includes a recording of an augmented reality-based conversation between one of the customers and one of the representatives.

28. The computer-implemented method of claim 1, wherein at least one of the multiple recordings includes an e-mail conversation between one of the customers and one of the multiple representatives.

29. A non-transitory computer-readable storage medium storing computer-readable instructions, comprising:

instructions for retrieving multiple recordings of conversations associated with multiple representatives, wherein each conversation includes at least one of multiple customers and at least one of the representatives, and wherein each conversation is associated with a deal offered by the at least one of the representatives to the at least one of the customers;

instructions for extracting multiple features from each of the multiple recordings, wherein the multiple features indicate characteristics of any of (a) a customer of multiple customers in the corresponding conversation, (b) a representative of multiple representatives in the corresponding conversation, (c) the corresponding conversation;

instructions for analyzing the features of each of the conversations to determine if a first conversation of the conversations includes any feature that is indicative of a first deal associated with the first conversation being at a risk; and

instructions for generating, based on the analyzing, information regarding a set of deals that is at risk, wherein each deal of the set of deals is determined to be at risk in an event one or more conversations with which the corresponding deal is associated includes features that are indicative of the corresponding deal being at risk, the generating further including generating a reason why each deal of the set of deals is at risk.

30. The non-transitory computer-readable storage medium of claim 29, wherein the instructions for analyzing the features to determine if the first deal is at risk includes instructions for determining, based on a usage of a specified word or a phrase by a customer associated with the first conversation, a set of features that is indicative of the first deal being at risk.

31. The non-transitory computer-readable storage medium of claim 30, wherein the instructions for generating the reason based on the set of features include:

instructions for generating the reason as at least one of lack of budget for closing the first deal, lack of authority for closing the first deal, lack of time to review the first deal, the first deal not being a priority to the customer, or a timeline for closing the first deal.

32. A system, comprising:

a first component that is configured to extract multiple features from each of multiple recordings of conversations, wherein each conversation includes at least one of multiple customers and at least one of the representatives, and wherein each conversation is associated with a deal offered by the at least one of the representatives to the at least one of the customers;

a second component that is configured to analyze the features of each of the conversations to determine if a first conversation of the conversations includes any feature that is indicative of a first deal associated with the first conversation being at a risk; and

a third component that is configured to generate information regarding a set of deals that is at risk, wherein each deal of the set of deals is determined to be at risk in an event one or more conversations with which the corresponding deal is associated includes features that are indicative of the corresponding deal being at risk, the generating further including generating a reason why each deal of the set of deals is at risk.