Amdocs intelligent decision automation
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Amdocs Intelligent Decision Automation. AIDA and Guided Interaction Advisor to be announced Feb 14, 2011. Amdocs – Bill Guinn, Mike Lurye, Susan MacCall December 7, 2010. Overview for Gartner . The Leader in Customer Experience Systems Innovation. $3.0B in revenue $410M operating income

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Amdocs Intelligent Decision Automation

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Amdocs intelligent decision automation

Amdocs Intelligent Decision Automation

AIDA and Guided Interaction Advisor to be announced Feb 14, 2011

Amdocs – Bill Guinn, Mike Lurye, Susan MacCall

December 7, 2010

Overview for Gartner


The leader in customer experience systems innovation

The Leader in Customer Experience Systems Innovation

  • $3.0B in revenue

  • $410M operating income

  • $1.4B in cash

  • More than 19,000 employees

  • Over 60 countries

A Unique Business Model

Service Provider Industry Focus

Leading BSS, OSS & Service Delivery Products

Strategic Services

TM Forum’s “Industry Leadership” award.

(June 2010)

Highest Possible Overall Rating in BSS Scorecard and OSS Scorecard Reports.

(Dec 2009, Jan 2010)

Leading Telecom Operations Management Systems Vendor Worldwide.

(May 2010)


What is amdocs intelligent decision automation aida

What Is Amdocs Intelligent Decision Automation (AIDA)?

  • Amdocs Intelligent Decision Automation (AIDA) is a closed-loop, self-learning system that lets you…

    • See what happens, when it happens

    • Understand what it means to your business

    • Take action andenforce business policy – automatically, intelligently and in business real-time

See

  • By uniquely combining technologies AIDA is able to better understand and predict the behavior of customers, providing individualized treatment to achieve specific business targets:

    • Increasing RPU by selling them the right products

    • Decreasing churn by treating customers based on their specific likes, needs, and recent issues

    • Reducing cost of operations by proactively preventing issues and optimizing cross-channel care functions

Think

Act


Requirements

Requirements

  • Massive scale, billions of events/day -> 100M customers

  • Business real time infrencing and decisioning (100ms response time)

  • Uncoupled integration – open world model –

  • M:M:M : atomic ->abstraction : structured and unstructured data

  • Cohesive past, present, and real time view World View

  • True user managed and defined schema and decision factors (concepts)

  • User defined policy, decisions, and the logic to manage concepts

  • Closed loop learning: decision->actions->effect->learning

  • Low cost .05 customer/year


Aida runtime architecture

AIDA Runtime Architecture

Think

Act

See

Actions

Decision Engine

Events

Application Server - Space Based

Container

Amdocs

Event Collector

Container

Semantic Inference

Engine

andBusiness

Rules

Amdocs

Integration

Framework

Event

Ingestion

Events

Scheduled

Events

Bayesian

Belief

Network

Revenue

Mgt

Ordering

CRM

CRM

Other

System

Other

System

Other

Systems

“Sesame”

Performance cache

Network

Business

Intelligence

Other

System

Other

Systems

RDF

Triple Store DB

Data Sources

Any internal or external system

Structured or Unstructured

Operational Systems

Any operational system


How the aida inference engine works

How the AIDA Inference Engine Works

Semantic Concept Model

The model defines the concepts including the high level business concepts

Inference

Engine

andBusiness

Rules

Machine learning trained models

The model contains the relationship between concepts including the dependencies

BBN probabilistic model

Recommendation model ( Future)

Other Machine learning algorithms

Inference

  • When an event occurs the event handler rule fires for that event

    • Evaluates the event message

    • Evaluates the existing ontology

    • Determines which semantic instances to create or update

Bayesian

Belief

Network

When any data changes, the inference engine fires in a “When - Then” style of computing, updating all “Automatic” concepts. Custom concept rules are fired if necessary. This creates a chain of updates

When a “on demand” concept is needed the inference engine finds and computes all of the dependant concepts

Business policy Rules

Semantic Concept Model

Event

Create semantic concepts from events

Machine learning

Concept

Define a custom business concept

Action

A set of custom rules actioning business policy

When a concept is dependent on “machine learned” information the inference engine manages the invocation and timing of interfacing

Defines concepts and meaning

Defines relationships between concepts

Semantic model capabilities reasoning


Why a triple store why allegrograph

Why a triple Store, why Allegrograph


Amdocs intelligent decision automation

Composer Demo Application

Filter Map

CONSTANTS

+

BUSINESS ENTITIES & CONCEPTS

AIDA Business Composer

Library

TAG

PATH

RELATIONSHIP MAP

DEPENDENCY MAP

Search Map

Customer

ACTIONS

PROCEDURES

EVENTS

ENTITIES

Entity Name

Customer

Acquisition Date

Status

Status History

Type

Call Frequency

Churn Propensity

Collection Risk

Payment Pattern

Customer Accounts

Customer Action

Acquisition Date

Customer Bill

Customer Accounts

Status

Customer Devices

Customer Bill

Status History

Customer Problems

+

+

+

Type

Customer Problems

Customer

Description. The role played by an Individual or Organization in a business relationship with the service provider in which they intend to buy, buy, or receive products or services from the service p

-

Customer

Call Frequency

-

Interactions

Churn Propensity

Collection Risk

Subscriptions

Payment Pattern


Amdocs intelligent decision automation

Composer Demo Application

Filter Map

CONSTANTS

+

BUSINESS ENTITIES & CONCEPTS

AIDA Business Composer

Library

TAG

PATH

RELATIONSHIP MAP

DEPENDENCY MAP

Search Map

Customer

ACTIONS

PROCEDURES

EVENTS

ENTITIES

Entity Name

Customer

Acquisition Date

Status

Status History

Customer Accounts

Type

Customer Bill

Call Frequency

+

Customer Problems

Churn Propensity

-

Customer

-

Collection Risk

Interactions

Customer Payment Pattern

Payment Pattern

Subscriptions

Customer Accounts

VIEW

EDIT

DEPLOY

Customer Action

Acquisition Date

Customer Bill

Customer Payment Pattern

Status

Customer Devices

LAST MODIFIED: 02/12/2010

MODIFIED BY: Greg Sloan

VERSION: 1.3

COMPUTED:Automatically

Status History

Customer Problems

DESCRIPTION: Determine the payment pattern for the customer by evaluating payments determining good payer, bad payer, worsening payer or improving payer

+

+

Type

Customer

Description. The role played by an Individual or Organization in a business relationship with the service provider in which they intend to buy, buy, or receive products or services from the service p

Call Frequency

Find

all of the

Previous Bills

Within

6

months

1.

Churn Propensity

Collection Risk

Payment Pattern

2.

If

all bills

have

Payment Timeliness

equal to

early

then

Payment Pattern

is

good payer

3.

If

all bills

have

Payment Timeliness

equal to

late

otherwise

then

Payment Pattern

is

bad payer

4.

If

at least

75%

of

Earlier Bills

are early or on time

otherwise

and

later bills

are

late

then

Payment Pattern

is

worsening payer

5.

If

At least

50%

of

Earlier Bills

Late

are

and

all later bills

are

On time or early

then

Payment Pattern

is

Improving payer


The applications

The Applications

Virtual Agent (GA in September)

Guided Interaction Advisor (GA in May)


Guided interaction advisor gia

A pre-built Ontology and rule set in AIDA designed to address key issues in call center interaction

Amdocs Guided Interaction Advisor

Anticipates reason for the customer interaction

Then Automates access to the required information and Guidesthe flow of action and decision making

Business Benefits

Eliminates system and agent diagnosis time

Provides consistent and efficient call handling

Increases agent and customer satisfaction

Anticipated benefits based on 100K actual accounts assessment:

AHT reduction of 10-15%

FCR improvement of 10-15%

CSR training time reduction of 15-20%

Guided Interaction Advisor (GIA)


How guided interaction advisor reduces aht example

and

and

and

and

and

and

and

and

How Guided Interaction Advisor Reduces AHTExample

* In addition to displaying the bill, GIA returns multiple “next” actions such as dispute charge, adjust the fee, make payment depending on high level concepts and policy

GIA Looks at events including bill sent event

Finds abnormal fee – Returned Check

Interactions

Probability that the call is motivated by fee vs. other issues

Compute High-level concepts

LOGIC ZOOM:

LOGIC ZOOM:

VIEW

VIEW

EDIT

EDIT

DEPLOY

DEPLOY

simple

simple

advanced

advanced

Examine company policy for treating this customer

Late

Abnormal Fee Policy

VERSION: 1.3

VERSION: 1.3

OWNER: User Name

OWNER: User Name

Dollars

MODIFIED BY: User Name

MODIFIED BY: User Name

DESCRIPTION: Loremipsum dolor sit amet, consecteturadipiscingelit. Sedcondimentum ante euturpisplaceratporttitor. Maecenas vitae lectus non justomollisrutrum at condimentum mi. [Edit]

DESCRIPTION: Loremipsum dolor sit amet, consecteturadipiscingelit. Sedcondimentum ante euturpisplaceratporttitor. Maecenas vitae lectus non justomollisrutrum at condimentum mi. [Edit]

1.

1.

Determine actions for each probable issues

If

If

Customer has a late Fee

Customer has a late Fee

and

and

Customer Payment Pattern

Customer Payment Pattern

is

is

Good Payer

Good Payer

or

or

Improving Payer

Improving Payer

and

and

Customer Value

Customer Value

is not

is not

Low

Low

3

3

then

then

Credit Fee

Credit Fee

.

.

Presents contextually relevant info, streamlined action

Other

Amount Received

Amount Received

Partial Payment

Partial Payment

Late Fee

Late Fee

Payment

Payment

Credit Fee

Credit Fee

Method

Method

Customer Payment Pattern

Customer Payment Pattern

Payments

Policy action rules

CIM actions

Customer Bill

Motivation for call

  • Abnormal Fee 47%

  • Pay Bill 23%

  • Device Exchange 12%

  • Customer Cancel 5%

  • Bill Due Date is Oct 1

  • Third Party Charges are normal

  • Charges are not typical

  • Bill has charges for fees.

  • Bill has abnormal fee

  • fee is “Returned check”

  • Tax amount is normal

*

Abnormal fee

Display bill

Highlight fee on bill

Script message

Prepare one click action

Highlight one click action

Pay Bill

Educate free on-line pay

Prepare one click pay

Assess

Probability

Customer

Graph

High level Concepts

  • High value customer

  • Long time customer

  • Improving payer

  • Contract expiring

Guided Interaction Advisor


Guided interaction advisor

Guided Interaction Advisor

Predictions & Actions

Events collected in real time

Transformed into a connected

graph of business concepts

Plan Overage

Bill greater than last month

Prorates

Interactions

Roaming charges

Orders

Third Party Charges

Bills

Abnormal fee

Payments

Rate increases

Charge dispute

Collections

First bill

Charge dispute

Past due amount

Customer

Pay bill

Probability of call motivation

Customer Cancellation

Pay instructions

Reactivation

Individual

Device activation

Device Activated

Device education

Device heartbeat

Device exchange

Device Lost

Subscriptions

Device not working

Chronology of events

Device changes

Subjective "good payer"

Patterns "always pays 2 days late"

Trends“improving payer"

Geospatial “within 5 miles of the tower"

Time “within 5 minutes of an outage"

Probability “probably will call about the bill"

Absence of occurrence “missed payment”

Highly User extensible …

Device resume

Extensible

Service data not working

Service text not working

Service voice not working

Use Case Extensions


Presenting insight to csr

Presenting Insight to CSR

Process opens

relevant screen for reference and action

Prediction on reason for the call – ranked by probability

Recommended actions – based on best ROI

Prioritized Recommended treatment and script


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