Mining interactions to manage customer experience throughout a customer service lifecycle

• US 8,396,741 B2
• Filed: 10/22/2009
• Issued: 03/12/2013
• Est. Priority Date: 02/22/2006
• Status: Active Grant

• Current Assignee: [24]7.ai, Inc.
• Original Assignee: 24/7 Customer Incorporated
• Inventors: Kannan, Pallipuram V., Jain, Mohit, Vijayaraghavan, Ravi, Albert, Ponnusamy Sam, Amudhan, Vijayalakshmi
• Primary Examiner(s): Jasmin, Lynda
• Assistant Examiner(s): Padot, Timothy
• Application Number:
  US12/604,252
• Publication Number:
  US 20100138282A1
• Time in Patent Office: 1,237 Days
• Field of Search: 705/7.38, 705/7.41, 705/7.42, 379/265.06
• US Class Current: 705/7.42
• CPC Class Codes: G06Q 10/06398 Performance of employee with respect to a job function
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A customer experience is improved through data mining and text mining technologies and that derive insights about a customer by analyzing interactions between the customer and a customer service agent. One or more numerical measurements of customer satisfaction are derived and recommended actions are provided to an agent to enhance the customer experience throughout a customer service lifecycle.

Claims

1. A computer-implemented method for managing a customer experience throughout a customer service lifecycle, comprising the steps of:

providing a customer-agent interaction platform;

conducting an interaction between at least one customer and at least one customer service representative on said customer-agent interaction platform;

providing a data fusion engine for processing data relevant to a customer experience, wherein said data fusion engine comprises a processor, a memory device coupled to said processor, and a network interface,

gathering, by the processor, said data relevant to a customer experience from one or more sources, wherein said one or more sources comprise at least said customer-agent interaction platform;

ingesting said data;

processing, by the processor, said data using one or more processing modules, forming one or more insights, wherein the step of processing said data further comprises the steps of;

recording said interaction data;

transcribing said recorded interaction data into a textual record;

text mining said textual record for indicators of positive or negative customer experience;

using said indicators to determine a text-mining net experience score (NES);

performing at least one follow up survey following said interaction, the follow up survey comprised of a questionnaire asking a survey-taker to respond to predetermined questions, wherein said follow up survey yields results comprising unstructured data and structured data, wherein said structured survey data is analyzed and given a customer satisfaction (CSAT) score, wherein said unstructured data is data-mined for data relevant to the customer experience and given a survey NES, and wherein said CSAT score and said survey NES are combined;

extracting common results between the text-mining NES and said combined CSAT and survey NES scores, forming an overall NES;

using said overall NES to analyze the effectiveness of said at least one customer service representative during said interaction;

providing feedback to said at least one customer service representative relating to said effectiveness; and

outputting one or more insights.

2. The method of claim 1, wherein said interaction is conducted via any of a phone call, an email, and an instant messaging session.

3. The method of claim 1, wherein said one or more sources are selected from among a group of sources comprising any of:

said at least one customer;

said at least one customer service representative;

said interaction platform;

one or more community-sponsored data sources;

one or more industry data sources;

one or more survey sources;

one or more third party databases; and

one or more internal databases.

4. The method of claim 1, wherein the step of processing said data using one or more processing modules further comprises the step of data mining the data relevant to customer experience from one or more sources.

5. The method of claim 1, wherein the step of processing modules comprises using a processing module selected from among a group of processing modules comprising any of:

a problem type/query type categorization module;

a web text mining module;

an area of customer service improvement opportunity identification module;

a cognitive category model module;

a neurolinguistics processing module;

a statistical modeling module;

an insight generation module;

a competition analysis module;

a chat summarization module;

a proactive problem prediction module;

an agent survey module; and

combinations thereof.

6. The method of claim 1, wherein said data relevant to customer experience comprises data selected from among a group of data comprising any of:

customer-agent interaction data;

Customer Relationship Management (CRM) data;

geography indicator information;

customer history;

customer demographics;

data from online forums;

data from blogs;

data from short message format personal media feeds; and

combinations thereof.

7. An apparatus for managing a customer experience throughout a customer service lifecycle, comprising:

a customer-agent interaction platform for conducting an interaction between at least one customer and at least one customer service representative and for memorializing said one or more interactions, in the form of interaction data;

a survey module for conducting one or more surveys and for memorializing the results of said one or more surveys in the form of survey data;

a data fusion engine for gathering interaction data and survey data that is relevant to said customer experience, and for processing the gathered data, wherein the data fusion engine comprises;

a processor;

a memory device coupled to said processor;

a network interface; and

one or more customer experience improvement modules configured for ingesting said gathered data, and outputting one or more insights;

wherein at least one of the one or more processing modules comprise a dynamic feedback loop module, wherein said dynamic feedback loop module is configured for;

recording said interaction data;

transcribing said recorded interaction data into a textual record;

text mining said textual record for indicators of positive or negative customer experience; and

using said indicators to determine a text-mining net experience score (NES);

performing at least one follow up survey following said interaction, the follow up survey comprised of a questionnaire asking a survey-taker to respond to predetermined questions, wherein said follow up survey yields results comprising unstructured data and structured data, wherein said structured survey data is analyzed and given a customer satisfaction (CSAT) score, wherein said unstructured data is data-mined for data relevant to the customer experience and given a survey NES, and wherein said CSAT score and said survey NES are combined;

extracting common results between the text-mining NES and said combined CSAT and survey NES scores, forming an overall NES;

using said overall NES to analyze the effectiveness of said at least one customer service representative during said interaction; and

providing feedback to said at least one customer service representative relating to said effectiveness.

8. The apparatus of claim 7, wherein said one or more surveys are conducted via any of a phone call, an email, a website, and a text message.

9. The apparatus of claim 7, wherein said interaction comprises any of a phone call, an email session, and an instant messaging session.

10. The apparatus of claim 7, wherein said one or more surveys are selected from among a group of sources comprising any of:

said at least one customer;

said at least one customer service representative;

said interaction platform;

one or more community-sponsored data sources;

one or more industry data sources;

one or more survey sources;

one or more third party databases; and

one or more internal databases.

11. The apparatus of claim 7, wherein at least one of the one or more processing modules comprise a data mining module for data mining the data relevant to customer experience from one or more sources.

12. The apparatus of claim 7, wherein at least one of the one or more processing modules comprise a processing module selected from among a group of processing modules comprising any of:

a problem type/query type categorization module;

a web text mining module;

an area of customer service improvement opportunity identification module;

a cognitive category model module;

a neurolinguistics processing module;

a statistical modeling module;

an insight generation module;

a competition analysis module;

a chat summarization module;

a proactive problem prediction module;

an agent survey module; and

combinations thereof.

13. The apparatus of claim 7, wherein said data relevant to customer experience comprises data selected from among a group of data consisting of:

customer-agent interaction data;

Customer Relationship Management (CRM) data;

geography indicator information;

customer history;

customer demographics;

data from online forums;

data from blogs;

data from short message format personal media feeds; and

combinations thereof.