System and method for voice of the customer integration into insightful dimensional clustering

• US 20110016069A1
• Filed: 04/19/2010
• Published: 01/20/2011
• Est. Priority Date: 04/17/2009
• Status: Abandoned Application

• Current Assignee: Adrian Fan, Don Kainer, Eric A. Johnson
• Original Assignee: Adrian Fan, Don Kainer, Eric A. Johnson
• Inventors: Fan, Adrian, Johnson, Eric A., Kainer, Don
• Application Number:
  US12/799,216
• Publication Number:
  US 20110016069A1
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/60 Business processes related to postal services shipping G06Q10/083; franking apparatus G07B17/00

Abstract

A computer implemented method utilizing Insightful Dimensional Clustering (IDC) to extrapolate characteristics of subscribers that contact customer care to the entire subscriber base is disclosed. The combination of interaction content and IDC results in the spread of rich interaction characteristics to the entire subscriber base resulting in groupings of subscribers with similar characteristics including behavior, preferences, complaints, churn propensities, and pertinent retention offers.

Claims

1. A computer implemented method for identifying a segment of a population having a target behavior within a larger population within a data space using customer contact center analytics, the method using a clustering algorithm, said method comprising:

identifying a plurality of dimensions to be used in the clustering algorithm;

creating a plurality of category mentions for categorizing contact with a customer contact center;

recording interactions within a segment of population that contacts the customer contact center;

categorizing the interactions into the plurality of category mentions based upon predetermined words;

sorting the plurality of category mentions to identify one or more highly mentioned interaction categories;

narrowing the number of dimensions to be used in the clustering algorithm from a plurality of dimensions to a set of initial dimensions based upon at least one of the plurality of category mentions;

defining a tag from exemplar data within the data space from the population having a known behavior;

refining the tag based upon insights from at least one of the plurality of category mentions;

segmenting the population within the data space into clusters using the clustering algorithm;

analyzing the resulting clusters for a high tag concentration; and

displaying the resulting clusters.

2. The computer implemented method of claim 1, wherein the predetermined words are grouped into one or more predetermined phrases.

3. The computer implemented method of claim 1, wherein an interaction analytic engine performs the step of categorizing the interactions by first transcribing the contact with the customer contact center.

4. The computer implemented method of claim 1, further comprising the step of customizing categories to provide specificity and detail concerning the reason for a contact with the customer contact center.

5. The computer implemented method of claim 1, wherein the plurality of category mentions are chosen from a group inclusive of billing, services, equipment, usage, competitive mention and dissatisfaction.

6. A computer implemented method for identifying one or more customers having a target behavior within a larger population of customers within a telecommunication carrier data space using customer contact center analytics, the method using a clustering algorithm, said method comprising:

identifying a plurality of dimensions to be used in the clustering algorithm from a plurality of transactional variables within a transactional database;

creating a plurality of category mentions for categorizing contact with a customer contact center;

recording interactions with a plurality of customers that contact the customer contact center;

categorizing the interactions with the plurality of customers that contact the customer contact center into the plurality of category mentions based upon predetermined words;

sorting the plurality of category mentions to identify one or more highly mentioned interaction categories;

narrowing the number of dimensions to be used in the clustering algorithm from the plurality of dimensions to a set of initial dimensions using the transactional variables related to the highly mentioned interaction categories;

defining the tag from the highly mentioned interaction categories;

segmenting the population of customers within the data space into clusters using the clustering algorithm;

analyzing the resulting clusters for a high tag concentration so as to identify dimensions having a defined variance to the tag; and

displaying the resulting clusters.

7. The computer implemented method of claim 6, further comprising the step of performing an iterative process to determine the identity of dimensions that are important to the tag from the initial dimensions and the identity of the customers having a target behavior.

8. The computer implemented method of claim 6, further comprising:

identifying the one or more customers having a target behavior from the relationship between the tag and the initial dimensions; and

offering an action from the telecommunications carrier that addresses the target behavior.