APPARATUS AND METHOD FOR PREDICTING CUSTOMER BEHAVIOR

• US 20090222313A1
• Filed: 02/24/2009
• Published: 09/03/2009
• Est. Priority Date: 02/22/2006
• Status: Active Grant

• Current Assignee: [24]7.ai, Inc.
• Original Assignee: 24/7 Customer Incorporated
• Inventors: Jain, Mohit, Vijayaraghavan, Ravi, Kannan, Pallipuram V.
• Granted Patent: US 9,129,290 B2
• Time in Patent Office: Days
• US Class Current: 705/9
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/016 After-sales
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A predictive model generator that enhances customer experience, reduces the cost of servicing a customer, and prevents customer attrition by predicting the appropriate interaction channel through analysis of different types of data and filtering of irrelevant data. The model includes a customer interaction data engine for transforming data into a proper format for storage, data warehouse for receiving data from a variety of sources, and a predictive engine for analyzing the data and building models.

Claims

1. An apparatus for generating a prediction of customer behavior, comprising:

a customer interaction data engine for transforming data received from a plurality of sources into a format acceptable for storage, said data comprising data relating to a customer interaction arising from a purchase of a product or service, data relating to a product or service purchased by a customer, data about a plurality of customers, and data about a plurality of agents;

a data warehouse coupled to said customer interaction data engine, said data warehouse for storing said transformed data;

a predictive engine coupled to said data warehouse, said predictive engine for receiving said data from said data warehouse, for compiling said data by determining which variables are contributing variables, and for using said contributing variables to generate predictive models, said predictive engine identifying trends in customer behavior as a function of a product or service purchased by customers, time that has elapsed from a time said product or service was purchased, customer location, a problem associated with said product or service, and customer impact associated with said problem associated with said product or service.

2. The apparatus of claim 1, wherein said customer interaction data engine receives unstructured data from conversations between an agent and a customer and said predictive engine generates variables for predicting customer behavior based on said text.

3. The apparatus of claim 1, wherein said customer interaction receives unstructured data from conversations between an agent and a customer and merges said unstructured data into said format acceptable for storage.

4. The apparatus of claim 1, said customer interaction engine for extracting keywords from interactions between an agent and a customer, said keywords indicative of at least agent cognitive capabilities, agent emotions, and customer emotions, said customer interaction engine calculating a net experience score using said keywords.

5. The apparatus of claim 1, wherein said problem data comprises at least a unique identification number associated with a particular problem, a description of said problem, a category, a sub-category, and customer impact.

6. The apparatus of claim 1, wherein said product data comprises at least a unique identification number associated with each product, a product, a category, a sub-category if applicable, information about launch of said product or service, and notes.

7. The apparatus of claim 1, wherein said customer data comprises at least a unique identification number associated with each customer, a company name, a location, a phone number, a role contact, an account start date, a first shipment date, a last shipment date, a number of products or services purchased by each customer in a last month, a number of products or services purchased in a last year, a statistics code, a credit limit, and a type of industry associated with a customer.

8. The apparatus of claim 1, wherein said agent data comprises at least a unique identification number associated with each agent, each agent'"'"'s name, each agent'"'"'s skill sets, each agent'"'"'s relevant experience, interactions per day, customer satisfaction scores, gender, average handle times, holding time, talk time, call outcome, call satisfaction, and experience in a process.

9. The apparatus of claim 1, further comprising a user interface for allowing a user to generate models.

10. The apparatus of claim 1, further comprising a user interface for receiving information from a customer for said predictive engine to use in comparing said customer to said model.

11. A computer-implemented method for generating a model for predicting customer behavior, the method comprising the steps of:

receiving at a customer interaction data engine problem data comprising data relating to any customer interaction arising from a purchase of a product or service;

receiving at said customer interaction data engine product data comprising data relating to a product or service purchased by a customer;

receiving at said customer interaction data engine customer data comprising information about a customer;

receiving at said customer interaction data engine agent data comprising information about an agent;

transforming with said customer interaction data engine said problem data, product data, interaction data, and agent data into a storage format;

storing said data on a computer readable storage medium in a data warehouse;

determining with a predictive engine contributing variables;

building with said predictive engine a plurality of predictive models;

testing and validating said plurality of predictive models;

receiving a request to generate a predictive model for a customer; and

generating said customer predictive model in real time.

12. The method of claim 11, wherein said contributing variables are determined by applying the naï

ve Bayes algorithm to said data.

13. The method of claim 11, further comprising identifying with a processor trends in customer behavior as a function of a product or service purchased by customers, time that has elapsed from a time said product or service was purchased, customer location, a problem associated with said product or service, and customer impact associated with said problem associated with said product or service;

and

generating a model that predicts a mode for interacting with a customer to minimize cost associated with said interaction based on said trends in customer behavior.

14. The method of claim 11, further comprising the steps of:

presenting a customer with a user interface for asking said customer to identify at least one of said customer'"'"'s name, a product or service purchased by said customer, and a time of purchase;

comparing said customer to said model; and

predicting a mode of interaction with said customer that minimizes a cost of interacting with said customer.

15. The method of claim 14, further comprising the steps of:

responsive to identifying said mode of interaction as a phone call with an agent, providing said customer with a number to call said agent;

identifying a specific agent based on said agent'"'"'s experience level; and

providing said customer with a number of said specific agent.

16. The method of claim 11, further comprising the steps of:

receiving with said customer data interaction engine text files from conversations between an agent and a customer;

extracting keywords from said text files that are indicative of agent cognitive capabilities, agent emotions, and customer emotions;

weighting said keywords; and

calculating a net experience score based on said weighted keywords.

17. The method of claim 11, wherein said mode for interacting with said customer comprises telephone, internet chat, email, and web.

18. The method of claim 12, wherein said trends in customer behavior further comprise a function of an agent, an agent'"'"'s gender, and an agent'"'"'s experience.

19. The method of claim 11, wherein said predictive engine is configured to generate a model comprising at least one of a text table, aligned bars, stacked bars, discrete lines, scatter plots, Gantt charts, heat maps, side-by-side bars, measure bars, circles, scatter matrices, and histograms.

20. An apparatus for generating a prediction of customer behavior, comprising:

a customer interaction data engine for transforming data received from a plurality of sources into a format acceptable for storage, said data comprising data relating to a customer interaction arising from a purchase of a product or service, data relating to a product or service purchased by a customer, data about a plurality of customers, and data about a plurality of agents;

a data warehouse coupled to said customer interaction data engine, said data warehouse for storing said transformed data;

a predictive engine coupled to said data warehouse, said predictive engine for receiving said data from said data warehouse, for compiling said data by determining which variables are contributing variables, and for using said contributing variables to generate predictive models, said predictive engine receiving a request for a predictive model and generating said predictive model in real time in response to said request