# US 2014/0192971 A1 · Method and apparatus for analyzing leakage from chat to voice

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- Number: US 2014/0192971 A1
- Document ID: US20140192971A1
- Status: Published application
- Kind: A1
- Application number: 14/149,768
- Filed: 2014-01-07
- Published: 2014-07-10
- Earliest claimed: 2013-01-08
- Assignees: 24/7 Customer, Inc.
- Applicants: 24/7 Customer, Inc.
- Inventors: R. Mathangi SRI; Nitin Kumar HARDENIYA; Vaibhav SRIVASTAVA; Ravi VIJAYARAGHAVAN
- Web page: https://patentgrep.com/patent/US20140192971A1

## CPC

- H04M 3/5175: {Call or contact centers supervision arrangements}
- G06F 40/166: Editing, e.g. inserting or deleting
- G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30)
- G06Q 30/016: After-sales
- H04M 3/5133: {Operator terminal details}
- H04M 3/5191: {interacting with the Internet}
- H04M 7/0045: {where the text-based messaging service is an instant messaging service}
- H04M 2207/203: composed of PSTN and data network, e.g. the Internet
- H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

The customer experience is enhanced by detecting leakage-to-voice from chats and providing recommendations to operations, chat agents, and customers. A chat is classified into leakage-to-voice or leakage-to-text chat and actionable recommendations are then provided to operations, chat agents, and customers based on the leakage information. Once leakage is identified, various other insights are extracted from chats and such insights are fed into the knowledge-base. Such insights also used in agent training and are provided to chat agents as recommendations. This results in a better customer experience.

## Claims

### Claim 1

A computer implemented method for analyzing chat leakage, comprising:
providing a processor configured for obtaining chat-related information from at least one chat session between a customer and an agent;
said processor configured for identifying customer leakage information from said chat to another channel;
said processor configured for building a model based on said chat-related information and said leakage information; and
said processor configured for applying said model to provide recommendations to said agent for said customer to improve the customer's experience and accordingly prevent or reduce leakage.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
when chat leakage is identified, analyzing said chat to determine factors that have contributed to said leakage;
storing data pertaining to said leakage and said analysis results in a knowledge base; and
using information and analysis thereof stored in said knowledge base to train agents and to make recommendations to agents and managers to improve the customer experience.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
classifying chats that are identified as having leakage into leakage-to-voice or leakage-to-text chats.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
said chat agent performing any of providing solutions to customer issues and redirecting said chat to other channels to resolve said customer issues.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
said chat agent checking information related to the customer, said information comprising any of the customer's journey, the customer's communication history, the customer's interests, and other information associated with the customer.

### Claim 6 (depends on 5)

The method of claim 5, wherein the customer's journey comprises any of:
identity of chat agents, either a voice or a text chat agent, who interacted with the customer before the customer visited a specific chat agent;
a path taken by the customer to reach a chat agent;
the customer's Web-log journey; and
other customer information.

### Claim 7 (depends on 5)

The method of claim 5, wherein the customer's journey comprises any of a virtual journey, a literal journey, an assisted journey, a guided journey, and a combination thereof.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
identifying a channel to which leakage occurs.

### Claim 9 (depends on 1)

The method of claim 1, further comprising:
using sample production data to build said model.

### Claim 10 (depends on 1)

The method of claim 1, building said model further comprising:
using a chat text to build an anchor.

### Claim 11 (depends on 10)

The method of claim 10 further comprising:
extracting channel names referred in the chat text to build said anchor.

### Claim 12 (depends on 10)

The method of claim 10, further comprising:
after said anchor is built, using positive hits generated during anchor building in connection with edit-distance to obtain a temporary categorization of a team/department.

### Claim 13 (depends on 12)

The method of claim 12, further comprising:
business analysts using business sense for each team/department and customer needs to perform an initial grouping of a portion of teams/departments.

### Claim 14 (depends on 12)

The method of claim 12, further comprising:
applying a service layer to fine tune category mapping of said team/department; and
generating a model that includes a final category for each team/department name.

### Claim 15 (depends on 14)

The method of claim 14, wherein said category comprises a categories grouping for each team/department name based on edit distance.

### Claim 16 (depends on 12)

The method of claim 12, further comprising:
after said anchor is built, categorizing said chat data into said team/department names.

### Claim 17 (depends on 16)

The method of claim 16, further comprising:
applying specific filters for voice and text chat categorization.

### Claim 18 (depends on 17)

The method of claim 17, wherein said filters and said team/department names occur in a periphery of a predefined number of words; and
said voice and text chat filters creating dummy data having a periphery of said predefined number of words, instead of using an entire agent chat text; and
providing said dummy data with a dummy identifier.

### Claim 19 (depends on 17)

The method of claim 17, further comprising:
providing a priority index to control each of said filters.

### Claim 20 (depends on 19)

The method of claim 19, further comprising:
providing a grid editor to provide said priority to said filters.

### Claim 21 (depends on 10)

The method of claim 10, wherein said chat text is any of text provided by said customer and by said agent.

### Claim 22 (depends on 16)

The method of claim 16, further comprising:
performing a noun extraction process in which a part-of-speech (pos) tagger is used to tag lines in the chat based on pos information.

### Claim 23 (depends on 22)

The method of claim 22, wherein a first noun from the line of chat text is extracted based on the hypothesis that most of the time the product name is mentioned as the first noun.

### Claim 24 (depends on 16)

The method of claim 16, further comprising:
looking up a concordance words with the anchors;
wherein said concordance comprises any of direction (left window/right window/around window) and the window size; and
wherein based on the direction, positive hits are generated by the anchor building process.

### Claim 25 (depends on 16)

The method of claim 16, further comprising:
performing a noun phrase extraction process in which a part-of-speech (pos) tagger is used to tag lines in the chat text based on pos information.

### Claim 26 (depends on 25)

The method of claim 25, wherein a first noun from the line of chat text is extracted based on the hypothesis that most of the time the product name is mentioned as the first noun.

### Claim 27 (depends on 16)

The method of claim 16, further comprising:
performing a stopword removal process to remove stopwords from the chat text.

### Claim 28 (depends on 16)

The method of claim 16, further comprising:
performing a get unique word process to remove multiple occurrences of the same team/department names.

### Claim 29 (depends on 16)

The method of claim 16, further comprising:
performing a surface-similarity process in which a surface similarity measure is used to determine edit-distance at a word level or at a character level; and
grouping team/department names in a category of the team/department based on similar words.

### Claim 30 (depends on 16)

The method of claim 16, further comprising:
including the agent's names as a variable for use in evaluating the agents' performance.

### Claim 31 (depends on 30)

The method of claim 30, further comprising:
mapping chat agent performance data with a customer satisfaction score (CSAT) driver and;
based thereon, providing actionable recommendations to operations and agents.

### Claim 32 (depends on 1)

The method of claim 1, further comprising:
passing contextual information to a voice channel.

### Claim 33 (depends on 1)

The method of claim 1, further comprising:
passing insights from one channel to a next channel.

### Claim 34 (depends on 1)

The method of claim 1, further comprising:
detecting leakage and extracting substantially all contextual and other relevant information from a chat interaction when there is a customer dropout during a chat and the agent cannot solve the customer problem; and
summarizing said relevant information and passing said summarized information to a voice referral.

### Claim 35 (depends on 1)

The method of claim 1, further comprising:
adding information regarding leakage to a Web-log journey to predict leakage at the start of a subsequent customer journey when leakage in a current chat is detected; and
providing recommendations to said agent at the start of said subsequent chat, based on leakage detected in said current chat.

### Claim 36 (depends on 1)

The method of claim 1, further comprising:
said processor configured for applying said model to provide off line training analysis.

### Claim 37

An apparatus for analyzing chat leakage, comprising:
a processor obtaining chat-related information from at least one chat session between a customer and a chat agent;
said processor identifying customer leakage information from said chat to another channel;
said processor building a model based on said chat-related information and said leakage information; and
said processor applying said model to provide recommendations to said chat agent to improve the customer's experience and accordingly prevent or reduce leakage.