Challenges and opportunities in customer

Transcript Challenges and opportunities in customer

Challenges and opportunities in customer-led services

James Taylor Fair Isaac Corporation

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 In the same way, over and over again While being able to adapt “on-the-fly”

PRECISION

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

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

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 Empowerment  Fewer approvals, faster decisions  More response-oriented  Third parties act like you  Customers can self-serve 4

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 Copyright © 2003 Fair Isaac Corporation. All rights reserved.

Adverse selection and micro-segmentation

Price Over/Under-priced segments Ideal Pricing Model

Risk

For what products will you see them?

 Information  Insurance  Banking  Credit  Mass-Customizable  Clothing  Electronics

Value Automated Decisions

 Long Tail  Books  Music  Content

Manual Decisions Complexity Expert Decisions

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 risk  Driver's age  Driving history  Commuting distance  Dynamic measures  Speed  Time of day  Location  A pricing band for every single policyholder they serve Copyright © 2003 Fair Isaac Corporation. All rights reserved.

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 Copyright © 2003 Fair Isaac Corporation. All rights reserved.

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 Copyright © 2003 Fair Isaac Corporation. All rights reserved.

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 Copyright © 2003 Fair Isaac Corporation. All rights reserved.

Technology Challenges

TECHNOLOGY Data Access & Management DESCRIPTION

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

Issues

Privacy Real-time

Descriptive Analytics

Analytics for analyzing and understanding individual and group behavior Demographics Data Sources

Predictive Analytics Business Rules Deployment

Analytics for predicting individual behavior and for identifying best actions to meet objective Legality Balance v choices Software for defining, testing and executing rules, processes and strategies Ownership Change management Integrating services into delivery processes and systems Auditing Process integration Third-party integration Copyright © 2003 Fair Isaac Corporation. All rights reserved.

What rules look like

If customer's debt exceeds customer’s assets then set the approval_status of customer’s application to Declined If flight’s onTimeReliability is less than 75% then flight’s valueToMe is “Low”.

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

Descriptive Models Identify Relations

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

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 Copyright © 2003 Fair Isaac Corporation. All rights reserved.

Predictive Models Calculate Risk Or Opportunity

Predictive models often rank-order individuals. For example, credit scores rank order borrowers by their credit risk for every “bad” one.

– the higher the score, the more “good” borrowers

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 18

Bringing this all to bear

Business Rules Rule & Model Repository Analytic Models 0 0 1

Decision Service Data Rules Models Decision Analysis ERP Request for Decision CRM Decision OPERATIONAL SYSTEMS Billing SCM Call Center Web Email Telemarketing http://ww CHANNELS Direct Mail Store / Branch Kiosk / ATM Field 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 Copyright © 2003 Fair Isaac Corporation. All rights reserved.

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  Subscribe to the blog(s) with RSS or email  E-mail me  [email protected]

 Ask me questions now!

Challenges and opportunities in customer

Transcript Challenges and opportunities in customer