Challenges and opportunities in customer-led services James Taylor Fair Isaac Corporation

Challenges and opportunities in customer-led services

James TaylorFair Isaac Corporation

2Copyright © 2003 Fair Isaac Corporation. All rights reserved.

Enterprise Decision Management

Enterprise Decision Management (EDM) is a systematic approach to

automate and improve decisions across the enterprise.

It allows businesses to:

Make more profitable and targeted

decisions

PRECISION

In the same way, over and over again

CONSISTENCY

While being able to adapt “on-the-fly”

AGILITY


Agenda

Customer-led services

What are they

Why are they going to happen

Where are they going to happen

Some examples

Present

Future

Challenges with customer-led services

Organizational

Technological

Ethical


Characteristics of Customer-led Services

Personalization

Rewards Loyalty

Analytic targeting

Rules for policies, preferences

Predictions of responses

Channel Consistency

Stronger customer relationships

Customers preferred channels

Customer value drives interaction

Pricing

Variable pricing

Multiple pricing mechanisms

Shared value established

Empowerment

Fewer approvals, faster decisions

More response-oriented

Third parties act like you

Customers can self-serve

http://www.f


Why Customer-led Services?

Growing important of information in products

Response to threats to traditional business from the explosion and prevalence of the Internet

Price transparency

Customer mobility and a lack of loyalty

The Long Tail

An opportunity to create competitive advantage from customer data


Adverse selection and micro-segmentation

Price

Risk

IdealPricingModel

Over/Under-priced segments


For what products will you see them?

Information

Insurance

Banking

Credit

Mass-Customizable

Clothing

Electronics

Long Tail

Books

Music

ContentComplexity

Value

Automated

Decisions

Expert

Decisions

Manual

Decisions

Manual D

ecisions


Current Example - Pay as you drive insurance

Logical extension of micro-segmentation Use of a far broader range of variables and predictive analytics Precisely rate the risk presented by individual consumers.

Static measures of riskDriver's ageDriving historyCommuting distance

Dynamic measuresSpeedTime of dayLocation

A pricing band for every single policyholder they serve


Current Example – Amazon.com


Getting closer with My amazon.com


Future Example - Personal online shopping

Site reconfigures itself to suit me

Explicitly through instructions (rules)

Implicitly though analysis (analytics)

Channels are integrated

Email, IM, Mobile, Phone, Store(s), Mashups

Choices and actions (or comments) in one affect the others

Offers, pricing, shipment are dynamic

Based on the specific purchase consideration

Loyalty is rewarded

If information is available that could improve my experience, it is used


Current Example – Online Banking


Future Example - Personal banking

The website does more than show my accounts It stops asking me to open accounts I have It stops asking for information for new accounts that it already has It makes recommendations on credit cards it does not just list them It feeds information about what I look at into offer models Pricing and offers are made in real time to suit me It makes it easy for me to do the things I always do And so on…

Meanwhile… The ATM remembers you and reconfigures itself The IVR reconfigures based on wait times, status, past behavior … The monthly statement highlights out of pattern activities Branch staff make intelligent suggestions based on your recent

behavior and the behavior of successful customers with a similar profile


Challenges in developing customer-led services

Organizational

Design, deployment, lifecycle, innovation...

Some banks now release hundreds of new products a month

Price transparency and intra-P&L pricing

Channel consistency

Ending the separation between back and front office

Ethical

Data privacy

Business mashups and privacy

Cross-border regulations


Technology Challenges

TECHNOLOGY DESCRIPTION Issues

Data Access & Management

Acquire, access, deliver and manage data from internal and external sources

Privacy

Real-time

Descriptive Analytics

Analytics for analyzing and understanding individual and group behavior

Demographics

Data Sources

Predictive Analytics

Analytics for predicting individual behavior and for identifying best actions to meet objective

Legality

Balance v choices

Business Rules

Software for defining, testing and executing rules, processes and strategies

Ownership

Change management

Auditing

Deployment Integrating services into delivery processes and systems

Process integration

Third-party integration


What rules look like

If (vehicle’s age is between 0 years and 8 years) and (policyholder’s age is between 21 years and 60 years) and (policyholder’s number_of_claims does not exceed 3) Then set policyholder’s case to “STANDARD”

If flight’s onTimeReliability is less than 75% then flight’s valueToMe is “Low”.

If customer's debt exceeds customer’s assets then set the approval_status of customer’s application to Declined


Descriptive Models Identify Relations

Use: Find the relationships between customers

Example: Sort customers into groups with different buying profiles.

Operation: Analysis is generally done offline, but the results can be used in automated decisions – such as offering a given product to a specific customer

Descriptive models can be used to categorize customers into different categories – which can be useful in setting strategies and targeting treatment.


Predictive Models Calculate Risk Or Opportunity

Use: Identify the odds that a customer will take a specified action

Example: Will the customer pay me back on time? Will the customer respond to this offer?

Operation: Models are called by a business rules engine to “score” an individual or transaction, often in real time

Predictive models often rank-order individuals. For example, credit scores rank-order borrowers by their credit risk – the higher the score, the more “good” borrowers for every “bad” one.


Models

Bringing this all to bear

Rules

AnalyticModels

BusinessRules

010

Decision Service

Rule & Model

Repository

Data

Request for

Decision

Decision

http://ww

Call Center

Web

Email

Telemarketing

CHANNELS

Direct Mail

Store / Branch

Kiosk / ATM

Field

ERP

CRM

OPERATIONAL SYSTEMS

Billing

SCM

Decision Analysis

Customer Behavior and

Strategy Performance


Fair Isaac Corporation –Automating decisions for 50 years

Founded in 1956

NYSE symbol: FIC

Annual revenues over $800 million

Market cap: Over $3 billion

3,000 employees

Software engineers, PhDs, data analysts, consultants…

Background in analyzing data, predicting outcomes, making decisions

Credit scoring

Customer acquisition / origination / management

Risk assessment

Fraud detection


Closing Thoughts

Consider customer-led service design

Think about micro-segmentation

Think about automation of decisions

Read my Blogs

Read my blog at http://www.edmblog.com

Read my (other) blog at http://www.eizq.net/blogs/decision_management

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Ask me questions now!

Documents

Challenges and opportunities in customer-led services James Taylor Fair Isaac Corporation