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Mining interactions to manage customer experience throughout a customer service lifecycle

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

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