# US 2019/0172069 A1 · Computer-based Understanding of Customer Behavior Patterns for Better Customer Outcomes

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- Number: US 2019/0172069 A1
- Document ID: US20190172069A1
- Status: Published application
- Kind: A1
- Application number: 16/210,081
- Filed: 2018-12-05
- Published: 2019-06-06
- Earliest claimed: 2017-12-05
- Assignees: discourse.ai, Inc.
- Applicants: discourse.ai, Inc
- Inventors: Jonathan E. Eisenzopf
- Web page: https://patentgrep.com/patent/US20190172069A1

## CPC

- G06Q 30/016: After-sales
- G06N 5/043: Distributed expert systems; Blackboards
- H04M 3/5175: {Call or contact centers supervision arrangements}
- H04M 3/5183: {Call or contact centers with computer-telephony arrangements}
- G06Q 30/04: Billing or invoicing
- H04M 3/523: with call distribution or queueing
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08)

## Abstract

An improved data processing system that continuously analyzes and automates a process of identifying statistically significant patterns of customer behavior linked to a specific set of customer outcomes and presenting these visually in a graph with linkages to the root causes, customer events, each step in the customer behavior, and the customer outcome. The improved computing system provides a set of hypotheses and recommendations based on the pattern matching solutions in a computer database and allows the user of the system to simulate the anticipated outcomes.

## Claims

### Claim 1

A method of improving a data processing system comprising:
continuously analyzing, by a processor, customer behavior linked to a specific set of customer outcomes;
identifying, by a processor, statistically significant patterns in the analyzed customer behavior;
matching, by a processor, the statistically significant patterns to one or more potential solutions in a computer database, wherein the potential solutions comprise one or more hypotheses, one or more recommendations, one or more root causes and one or more customer events correlated to the statistically significant patterns of customer behavior;
retrieving, by a processor, the potential solutions from the computer database; and
providing, by a processor, a simulation to a user for the one or more recommendations, wherein the simulation comprises at least one visualized graph having a plurality of linkages between the root causes, the customer events, each step in the customer behavior, and the customer outcome, and wherein the simulation provides a prediction of what one or more dominant customer paths would be with associated statistical significances for one or more specific events.

### Claim 2 (depends on 1)

The method as set forth in claim 1 wherein the analyzing of customer behavior comprises identifying and analyzing one or more relationships between potential root causes which drive events that cause customer behaviors related to a business or customer outcome having one or more tasks.

### Claim 3 (depends on 2)

The method as set forth in claim 2 wherein the analyzing of one or more relationships comprises, automatically and continuously, making specific observations and recommendations based on an expert database.

### Claim 4 (depends on 1)

The method as set forth in claim 1 further comprising determining, by a processor, customer behavior by correlating records, logs, and events from two or more disparate networked systems selected from the group consisting of a billing system, a customer account management web site, a Customer Relationship Management (CRM) system, an Automatic Call Distributor (ACD), an Interactive Voice Response (IVR) system, an Intelligent Assistant device, and a Private Branch eXchange (PBX) in a call center.

### Claim 5 (depends on 4)

The method as set forth in claim 4 further comprising classifying, by a processor, using raw data from the two or more disparate networked systems to correlate common customer paths to outcomes related to each root cause in the database.

### Claim 6 (depends on 5)

The method as set forth in claim 5 wherein the classifying comprises identifying, by a processor, similarities in data sets of the disparate networked system, and inferring, by a processor, the relationships

### Claim 7 (depends on 1)

The method as set forth in claim 1 further comprising:
aggregating, by a processor, a plurality of customer paths according to one or more criteria selected from the group consisting of a unique customer identifier, a class of customers, a customer type, a customer lifetime value, a customer total value spend, a previous outcome, a previous event, and a previous root cause;
comparing, by the processor, statistically common customer paths to one or more domain models;
extracting, by a processor, one or more observations from the comparable domain models; and
revising, by a processor, the domain models to reflect actual customer outcomes to more accurately predict future customer outcomes for similarly correlated tasks and events.

### Claim 8

A computer program product for improving a data processing system comprising:
a tangible, computer-readable memory device which is not a propagating signal per se; and
program instructions embodied by the tangible, computer-readable memory device for causing a processor to, when executed:
continuously analyze customer behavior linked to a specific set of customer outcomes;
identify statistically significant patterns in the analyzed customer behavior;
match the statistically significant patterns to one or more potential solutions in a computer database, wherein the potential solutions comprise one or more hypotheses, one or more recommendations, one or more root causes and one or more customer events correlated to the statistically significant patterns of customer behavior;
retrieve the potential solutions from the computer database; and
provide a simulation to a user for the one or more recommendations, wherein the simulation comprises at least one visualized graph having a plurality of linkages between the root causes, the customer events, each step in the customer behavior, and the customer outcome, and wherein the simulation provides a prediction of what one or more dominant customer paths would be with associated statistical significances for one or more specific events.

### Claim 9 (depends on 8)

The computer program product as set forth in claim 8 wherein the program instructions for analyzing of customer behavior comprise program instructions to identify and analyze one or more relationships between potential root causes which drive events that cause customer behaviors related to a business or customer outcome having one or more tasks.

### Claim 10 (depends on 9)

The computer program product as set forth in claim 9 wherein the analyzing of one or more relationships comprises, automatically and continuously, making specific observations and recommendations based on an expert database.

### Claim 11 (depends on 8)

The computer program product as set forth in claim 8 wherein the program instructions further comprise program instructions to determine customer behavior by correlating records, logs, and events from two or more disparate networked systems selected from the group consisting of a billing system, a customer account management web site, a Customer Relationship Management (CRM) system, an Automatic Call Distributor (ACD), an Interactive Voice Response (IVR) system, an Intelligent Assistant device, and a Private Branch eXchange (PBX) in a call center.

### Claim 12 (depends on 11)

The computer program product as set forth in claim 11 wherein the program instructions further comprise program instructions to classify using raw data from the two or more disparate networked systems to correlate common customer paths to outcomes related to each root cause in the database.

### Claim 13 (depends on 12)

The computer program product as set forth in claim 12 wherein the classifying comprises identifying similarities in data sets of the disparate networked system, and inferring, by a processor, the relationships

### Claim 14 (depends on 8)

The computer program product as set forth in claim 8 wherein the program instructions further comprise program instructions to:
aggregate a plurality of customer paths according to one or more criteria selected from the group consisting of a unique customer identifier, a class of customers, a customer type, a customer lifetime value, a customer total value spend, a previous outcome, a previous event, and a previous root cause;
compare statistically common customer paths to one or more domain models;
extract one or more observations from the comparable domain models; and
revise the domain models to reflect actual customer outcomes to more accurately predict future customer outcomes for similarly correlated tasks and events.

### Claim 15

An improved a data processing system comprising:
a computer processor;
a tangible, computer-readable memory device which is not a propagating signal per se; and
program instructions embodied by the tangible, computer-readable memory device for causing the computer processor to, when executed:
continuously analyze customer behavior linked to a specific set of customer outcomes;
identify statistically significant patterns in the analyzed customer behavior;
match the statistically significant patterns to one or more potential solutions in a computer database, wherein the potential solutions comprise one or more hypotheses, one or more recommendations, one or more root causes and one or more customer events correlated to the statistically significant patterns of customer behavior;
retrieve the potential solutions from the computer database; and
provide a simulation to a user for the one or more recommendations, wherein the simulation comprises at least one visualized graph having a plurality of linkages between the root causes, the customer events, each step in the customer behavior, and the customer outcome, and wherein the simulation provides a prediction of what one or more dominant customer paths would be with associated statistical significances for one or more specific events.

### Claim 16 (depends on 15)

The improved a data processing system as set forth in claim 15 wherein the program instructions for analyzing of customer behavior comprise program instructions to identify and analyze one or more relationships between potential root causes which drive events that cause customer behaviors related to a business or customer outcome having one or more tasks.

### Claim 17 (depends on 16)

The improved a data processing system as set forth in claim 16 wherein the analyzing of one or more relationships comprises, automatically and continuously, making specific observations and recommendations based on an expert database.

### Claim 18 (depends on 15)

The improved a data processing system as set forth in claim 15 wherein the program instructions further comprise program instructions to determine customer behavior by correlating records, logs, and events from two or more disparate networked systems selected from the group consisting of a billing system, a customer account management web site, a Customer Relationship Management (CRM) system, an Automatic Call Distributor (ACD), an Interactive Voice Response (IVR) system, an Intelligent Assistant device, and a Private Branch eXchange (PBX) in a call center.

### Claim 19 (depends on 18)

The improved a data processing system as set forth in claim 18 wherein the program instructions further comprise program instructions to classify using raw data from the two or more disparate networked systems to correlate common customer paths to outcomes related to each root cause in the database.

### Claim 20 (depends on 19)

The improved a data processing system as set forth in claim 19 wherein the classifying comprises identifying similarities in data sets of the disparate networked system, and inferring, by a processor, the relationships